Automobile Bluetooth positioning method and system

Abstract

The invention discloses an automobile Bluetooth positioning method, which comprises the following steps of: acquiring current environment information at set time intervals by a system in a parking state; after the environment information is obtained each time, calculating an environment compensation model based on the environment information to obtain compensation parameters; and the compensation parameters are transmitted to automobile Bluetooth positioning execution. According to the environment compensation model of the automobile Bluetooth positioning system, the RSSI dynamic adjustment processing is provided for the automobile digital key system, the use of the induction welcome function of each environment scene is comprehensively considered, and the problem of Bluetooth signal attenuation caused by weather influence is reduced.

Description

Technical Field

[0001] This invention relates to the field of intelligent control of automotive digital keys. Background Technology

[0002] With the rapid development of automotive technology and the government's vigorous promotion of new energy vehicles, digital key systems are also expanding. Bluetooth positioning systems are a new technology that has emerged and been widely adopted in the automotive industry in recent years. For example, the patent application with publication number CN116132959A, publication date May 16, 2023, entitled "Calibration Method, System, Device and Medium for Bluetooth Seamless Unlocking and Locking Function of Automobiles," discloses a method for calibrating mobile terminals. This method involves obtaining a first set of feature parameters from the mobile terminal to acquire a calibration feature database. The calibration feature database includes at least multiple sets of second feature parameters and calibration data packets corresponding to each set of second feature parameters. The method involves traversing the calibration feature database and determining if the first set of feature parameters is similar to any second set of feature parameters. Then, a calibration data packet corresponding to that second set of feature parameters is selected and sent to the mobile terminal.

[0003] However, similar vehicle-mounted Bluetooth contactless functions are significantly affected by weather conditions, which can impact Bluetooth signal strength (RSSI) and thus interfere with the accuracy of Bluetooth positioning. This makes it difficult for the unlocking / locking threshold settings to adapt to complex and changing environments. To more effectively ensure vehicle safety and the normal operation of the welcome function, an environmental model compensation mechanism is needed to fully meet the user's needs for sensor-based welcome functionality in various scenarios. Summary of the Invention

[0004] The technical problem to be solved by this invention is to realize an environmental compensation model based on car Bluetooth positioning, which can effectively ensure the safety of the whole vehicle and the normal use of the welcome function.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is: a car Bluetooth positioning method, wherein the system acquires current environmental information at set intervals when the car is parked;

[0006] Each time environmental information is obtained, compensation parameters are obtained by calculating an environmental compensation model based on the environmental information.

[0007] The compensation parameters are sent to the Bluetooth control unit for execution.

[0008] When the vehicle is parked in an underground parking garage or garage, environmental information is no longer periodically obtained, and the garage positioning method is continuously executed.

[0009] The environmental compensation model is pre-modeled based on different test scenarios for each vehicle model;

[0010] Tests were conducted in open environments under sunny, rainy, and different rainfall levels.

[0011] Test points at different distances were set up in the front, back, and side directions;

[0012] A mathematical model between weather factors and RSSI values ​​was established using multiple linear regression analysis. Weather factors included rainfall, sunshine, and temperature.

[0013] The test points include those at 1m, 3m, 5m, 8m, and 10m directly in front of the car; 1m, 3m, 5m, 8m, and 10m directly behind the car; 1m, 3m, 5m, 8m, and 10m to the left and right of the B-pillar; 1m, 3m, 5m, 8m, and 10m where the left front headlight is tilted to the left and forward; 1m, 3m, 5m, 8m, and 10m where the right front headlight is tilted to the right and forward; 1m, 3m, 5m, 8m, and 10m where the left rear headlight is tilted to the left and rear; and 1m, 3m, 5m, 8m, and 10m where the right rear headlight is tilted to the right and rear.

[0014] Before each test scenario begins, ensure that all sensors and test equipment are working properly and have been calibrated. Test personnel carry terminal devices to simulate a normal user's use of a Bluetooth key, staying at each test point for 30 seconds. During this time, RSSI values ​​are collected synchronously, and sensor data, including rainfall, light intensity, and temperature, are recorded synchronously via CAN. The test is repeated a set number of times for each distance point in each test scenario.

[0015] The mathematical model uses RSSI as the dependent variable and rainfall, light intensity, and temperature as independent variables to construct a regression equation: RSSI = a×rainfall + b×light intensity + c×temperature + d, where a, b, c, and d are regression coefficients. The regression coefficients are solved by the least squares method to minimize the sum of squared errors between the model's predicted values ​​and the actual measured values.

[0016] A vehicle Bluetooth positioning method system is provided. The system includes a sensor control unit, which outputs sensing parameters to a data acquisition module. The data acquisition module processes the collected data and sends it to an environmental compensation modeling module. The environmental compensation modeling module obtains compensation parameters based on the current data and sends them to the Bluetooth control unit for execution. The Bluetooth control unit communicates with the vehicle domain control unit. The system executes the vehicle Bluetooth positioning method as described in any one of claims 1-6.

[0017] The sensor control unit includes an ambient temperature sensor, a rain sensor, and a light intensity sensor located outside the vehicle.

[0018] The sensor control unit also includes a navigation device for acquiring positioning information and a camera for acquiring environmental image information.

[0019] The environmental compensation model of the car Bluetooth positioning system of this invention provides dynamic adjustment processing of RSSI for the car digital key system, comprehensively considers the use of the induction welcome function in various environmental scenarios, and reduces the problem of Bluetooth signal attenuation caused by weather. Attached Figure Description

[0020] The following is a brief explanation of the content represented by each figure in this specification:

[0021] Figure 1 This is a block diagram illustrating the principle of the environmental compensation model.

[0022] Figure 2 This is a schematic diagram for distance testing in an open environment. Detailed Implementation

[0023] The following description, with reference to the accompanying drawings, details the specific implementation of the present invention, including the shape and structure of each component, the relative positions and connections between the parts, the function and working principle of each part, the manufacturing process, and the operation and use methods, to help those skilled in the art to have a more complete, accurate, and in-depth understanding of the inventive concept and technical solution of the present invention.

[0024] The current Bluetooth positioning method for automobiles employs a data compensation approach. In in-vehicle Bluetooth contactless functionality, weather factors significantly impact Bluetooth signal strength (RSSI), interfering with positioning accuracy and making it difficult to adapt the unlocking / locking threshold settings to complex and changing environments. To improve the stability and reliability of Bluetooth keys under different weather conditions, a dynamic model is established to accurately describe the relationship between weather factors (rainfall, sunlight, temperature) and Bluetooth RSSI value changes, reliably predicting Bluetooth signal strength trends under various weather conditions. The model clarifies the variation patterns of Bluetooth signal strength with distance and weather factors under different weather scenarios (sunny days, rainfall levels), providing a scientific basis for the dynamic adjustment of Bluetooth positioning thresholds. Using dynamically adjusted Bluetooth positioning thresholds significantly improves the stability and reliability of the Bluetooth positioning system in complex weather environments.

[0025] The environmental compensation model of the car Bluetooth positioning system provides dynamic adjustment processing of RSSI for the car digital key system. It comprehensively considers the use of the induction welcome function in various environmental scenarios and improves the accuracy of Bluetooth recognition. Therefore, when the vehicle is parked, it obtains the current environmental information at set intervals. Whether the vehicle is parked can be obtained based on the lock signal. When the vehicle is locked and no one is in the car (no pressure signal from the seat pressure sensor), it is judged as parked. At this time, the vehicle environmental information is obtained. First, it is determined whether the vehicle is parked inside or outside the garage. The location information can be obtained through the navigation device to determine the parking location. Redundant judgment can also be performed. After obtaining the environmental image information, it is determined whether the parking lot is a sealed underground parking garage or an open-air parking lot like a rooftop. Only parking in a covered area is considered as parking inside the garage.

[0026] When it is determined that the car is parked in the garage, the system no longer periodically obtains environmental information, but continuously executes the garage positioning method. The garage positioning method is to immediately obtain the current parking lot environmental information, mainly temperature information, after determining that the car is parked in the garage, and compensate the Bluetooth control unit based on the temperature information.

[0027] If the car is parked in a non-garage area, environmental information will be acquired at set intervals after parking, and environmental information will also be acquired when parking. The interval can be set as needed, such as acquiring environmental information every 6 hours. After acquiring the environmental information, the environmental compensation model will be calculated based on the environmental information to obtain compensation parameters. In the following 6 hours, the compensation parameters will be sent to the Bluetooth control unit for execution.

[0028] When the Bluetooth control unit determines whether a user is approaching the car based on compensation parameters and the actual distance between the Bluetooth key and the car, the vehicle domain controller implements the intelligent welcome unlocking and locking function according to the preset Bluetooth control method. The compensation method can be simple: based on inclement weather conditions, the compensation value increases with worse weather and decreases with better weather. The compensation method is to subtract the compensation value from the current Bluetooth key distance. For example, if the compensation value calculated based on the weather parameters is 0.5m, and the Bluetooth control unit measures the key at 3 meters, then the key position is determined to be 2.5m.

[0029] The above environmental compensation model is based on pre-testing and modeling of different test scenarios for each vehicle model;

[0030] Different vehicle models need to be tested separately, and each test needs to simulate different test scenarios. Tests are conducted in open environments under sunny, rainy, and different rainfall levels. Sunny can be defined as rainfall of 0.

[0031] Test points at different distances were set up in the front, back, and side directions, respectively. Figure 2 As shown;

[0032] Test points include:

[0033] At 1m, 3m, 5m, 8m, and 10m directly in front of the car;

[0034] At 1m, 3m, 5m, 8m, and 10m directly behind the car;

[0035] At 1m, 3m, 5m, 8m, and 10m to the left and right of the B-pillar;

[0036] At 1m, 3m, 5m, 8m, and 10m where the left front headlight is angled to the left front;

[0037] At 1m, 3m, 5m, 8m, and 10m where the right front headlight is deflected to the right front;

[0038] At 1m, 3m, 5m, 8m, and 10m where the left rear headlight is tilted to the left and rear;

[0039] The distances from 1m, 3m, 5m, 8m, and 10m where the right rear headlight is angled to the right rear.

[0040] Before each test scenario begins, ensure all sensors and test equipment are functioning correctly and calibrated. Test personnel, carrying a terminal device, simulate a normal user experience using a Bluetooth key, staying at each test point for 30 seconds. During this time, RSSI values ​​are simultaneously collected, and sensor data, including rainfall, light intensity, and temperature, is recorded synchronously via CAN. For each test scenario, each distance point is tested 10 times to obtain statistically significant data samples and reduce the impact of random errors.

[0041] The environmental model was established using multiple linear regression analysis to create a mathematical model between weather factors (rainfall, sunshine, and temperature) and RSSI values. With RSSI value as the dependent variable and rainfall, sunshine intensity, and temperature as independent variables, the regression equation was constructed: RSSI = a × rainfall + b × sunshine intensity + c × temperature + d, where a, b, c, and d are regression coefficients. The regression coefficients were solved using the least squares method to minimize the sum of squared errors between the model's predicted values ​​and the actual measured values, thereby determining the quantitative relationship between weather factors and RSSI values. (The regression coefficients (a, b, c, d) represent the degree and direction of the influence of the independent variables on the dependent variable.)

[0042] In the multiple linear regression equation RSSI = a × rainfall + b × light intensity + c × temperature + d, for example, 'a' represents the average change in RSSI value by 'a' units for every unit increase in rainfall, assuming light intensity and temperature remain constant. If 'a' is positive, it indicates a positive correlation between rainfall and RSSI value; an increase in rainfall leads to an increase in RSSI value. If 'a' is negative, it indicates a negative correlation between the two.

[0043] b and c reflect similar effects of light intensity and temperature on RSSI values, respectively.

[0044] d is the intercept term, which represents the value of RSSI when rainfall, light intensity, and temperature are all zero. It is a constant term in the model.

[0045] For a set of observation data (xi1, xi2, xi3, yi), where i = 1, 2, ..., xi1, xi2, xi3 represent the rainfall, light intensity, and temperature of the i-th observation, respectively, and yi represents the corresponding RSSI value. According to the regression equation, the predicted value is yihat = axi1 + bxi2 + cxi3 + d; the error is ei = yi - yihat.

[0046] Supplementing the least squares method: Find a set of values ​​for a, b, c, and d that minimizes the sum of squared errors (SSE). By taking the partial derivatives of SSE with respect to a, b, c, and d respectively, and setting these partial derivatives to zero, a system of equations is obtained. Solving this system of equations yields the regression coefficients a, b, c, and d that minimize the sum of squared errors, thus determining the quantitative relationship between weather factors and RSSI values. A smaller sum of squared errors reflects the goodness of model fit; the closer the observed values ​​are to the predicted values, the better the model fits the data. Conversely, a larger sum of squared errors indicates a significant deviation between the model's predicted values ​​and the actual observed values, resulting in a poor model fit. The distribution characteristics of RSSI values ​​at different distances and their correlation with light and temperature are also discussed.

[0047] like Figure 1 As shown, the car Bluetooth positioning system includes a sensor control unit. The sensor control unit outputs sensing parameters to a data acquisition module, which processes the data acquired by the sensor control unit. The processed data is then sent to an environmental compensation modeling module. The environmental compensation modeling module obtains compensation parameters based on the current data and sends them to the Bluetooth control unit for execution. The Bluetooth control unit integrates a positioning algorithm and processes RSSI signals. The Bluetooth control unit communicates with the vehicle domain control unit. The vehicle domain controller processes the vehicle-sensing welcome signals fed back by the Bluetooth controller to realize the intelligent welcome unlocking and locking function.

[0048] The sensor control unit includes ambient temperature, rain, and light intensity sensors located outside the vehicle. Monitoring rainfall, light intensity, and temperature ensures that sensor data is transmitted to the vehicle for acquisition in real time. It also synchronously monitors the RSSI of each antenna, with a measurement range of -120dBm to 0dBm (accuracy ±1dBm), enabling synchronized data acquisition.

[0049] This invention plots RSSI values ​​as a function of distance, as well as scatter plots of RSSI values ​​against temperature and light intensity, and calculates correlation coefficients. It analyzes the relationship between RSSI values ​​and rainfall, temperature, and light intensity based on rainfall levels. Based on the established weather factors and RSSI variation model, a dynamic adjustment strategy for Bluetooth positioning thresholds under different weather conditions is formulated. For example, in rainy weather, when rainfall increases, the unlocking / locking threshold is appropriately lowered to compensate for the attenuation of the Bluetooth signal.

[0050] The present invention has been described above by way of example with reference to the accompanying drawings. Obviously, the specific implementation of the present invention is not limited to the above-described manner. Any non-substantial improvements made using the inventive concept and technical solution of the present invention, or the direct application of the inventive concept and technical solution of the present invention to other occasions without modification, are all within the protection scope of the present invention.

Claims

1. A car Bluetooth positioning method, characterized in that: When the system is parked, it acquires current environmental information at set intervals. Each time environmental information is obtained, compensation parameters are obtained by calculating an environmental compensation model based on the environmental information. The compensation parameters are sent to the Bluetooth control unit for execution.

2. The car Bluetooth positioning method according to claim 1, characterized in that: When the vehicle is parked in an underground parking garage or garage, environmental information is no longer periodically obtained, and the garage positioning method is continuously executed.

3. The vehicle Bluetooth positioning method according to claim 1 or 2, characterized in that: The environmental compensation model is pre-modeled based on different test scenarios for each vehicle model; Tests were conducted in open environments under sunny, rainy, and different rainfall levels. Test points at different distances were set up in the front, back, and side directions; A mathematical model between weather factors and RSSI values ​​was established using multiple linear regression analysis. Weather factors included rainfall, sunshine, and temperature.

4. The car Bluetooth positioning method according to claim 3, characterized in that: The test points include those at 1m, 3m, 5m, 8m, and 10m directly in front of the car; 1m, 3m, 5m, 8m, and 10m directly behind the car; 1m, 3m, 5m, 8m, and 10m to the left and right of the B-pillar; 1m, 3m, 5m, 8m, and 10m where the left front headlight is tilted to the left and forward; 1m, 3m, 5m, 8m, and 10m where the right front headlight is tilted to the right and forward; 1m, 3m, 5m, 8m, and 10m where the left rear headlight is tilted to the left and rear; and 1m, 3m, 5m, 8m, and 10m where the right rear headlight is tilted to the right and rear.

5. The vehicle Bluetooth positioning method according to claim 4, characterized in that: Before each test scenario begins, ensure that all sensors and test equipment are working properly and have been calibrated. Test personnel carry terminal devices to simulate a normal user's use of a Bluetooth key, staying at each test point for 30 seconds. During this time, RSSI values ​​are collected synchronously, and sensor data, including rainfall, light intensity, and temperature, are recorded synchronously via CAN. The test is repeated a set number of times for each distance point in each test scenario.

6. The vehicle Bluetooth positioning method according to claim 5, characterized in that: The mathematical model uses RSSI as the dependent variable and rainfall, light intensity, and temperature as independent variables to construct a regression equation: RSSI = a×rainfall + b×light intensity + c×temperature + d, where a, b, c, and d are regression coefficients. The regression coefficients are solved by the least squares method to minimize the sum of squared errors between the model's predicted values ​​and the actual measured values.

7. A car Bluetooth positioning method system, characterized in that: The system is equipped with a sensor control unit, which outputs sensing parameters to a data acquisition module. The data acquisition module processes the collected data and sends it to an environmental compensation modeling module. The environmental compensation modeling module obtains compensation parameters based on the current data and sends them to a Bluetooth control unit for execution. The Bluetooth control unit communicates with the vehicle domain control unit. The system executes the car Bluetooth positioning method as described in any one of claims 1-6.

8. The automotive Bluetooth positioning system according to claim 7, characterized in that: The sensor control unit includes an ambient temperature sensor, a rain sensor, and a light intensity sensor located outside the vehicle.

9. The automotive Bluetooth positioning system according to claim 8, characterized in that: The sensor control unit also includes a navigation device for acquiring positioning information and a camera for acquiring environmental image information.

Images