CN103017874B - Vehicle weight measuring method based on GPS (Global Position System) and inertial sensor - Google Patents

Abstract

In view of the above-mentioned problems and the application experience of the actual monitoring system, in order to realize the use effect of the whole process monitoring system, the present invention provides a vehicle weight measurement system based on GPS and inertial sensors that is low in cost and convenient for post-installation. The supervision and management of overloading violations include ARM embedded system and data acquisition module. The data acquisition module is connected to the acceleration sensor and angular velocity sensor, and the data acquisition module integrates the GPS module.

Description

Vehicle Weight Measurement Method Based on GPS and Inertial Sensor

technical field

The invention is suitable for the field of road transportation monitoring, and particularly relates to the monitoring technology for overweight. the

Background technique

With the continuous development of the national economy, the transportation industry has developed rapidly and its scale has also continued to expand. At the same time, the overload problem in my country's road transportation has become more and more serious, and it has become a prominent social problem that endangers the lives of the people and affects the coordination and healthy development of the social economy. However, due to the backwardness of vehicle weighing technology, the regulatory effect of overloading violations is not significant. Therefore, improving vehicle weighing technology has become a key issue. the

Currently commonly used vehicle weighing methods are mainly divided into two methods: external measurement and internal measurement:

1) Among the external measurement methods, one is the weighbridge at the monitoring station, which can obtain more accurate vehicle weight measurement, but it is not conducive to the monitoring by the supervision department; the other is to lay pressure sensors or photoelectric sensors at fixed places on the road for detection. The construction and maintenance of this method requires the construction of the road surface, which cannot meet the long-term and large-scale overload monitoring;

2) Among the internal measurement methods, various monitoring methods have been proposed at home and abroad. The main reason is to add pressure sensors to the elastic parts of the monitoring vehicles, which is inconvenient to install, which is not conducive to meeting the monitoring needs of a large number of vehicles, and is easily affected by errors and has poor robustness.

Contents of the invention

In view of the above-mentioned problems and the application experience of the actual monitoring system, in order to realize the use effect of the whole process monitoring system, the present invention provides a vehicle weight measurement system based on GPS and inertial sensors that is low in cost and convenient for post-installation. The need for supervision and management of overloading violations. the

In order to solve the above-mentioned technical problems, the technical solution adopted in the present invention is: provide a kind of vehicle weight measuring system based on GPS and inertial sensor, comprise ARM embedded system and data acquisition module, it is characterized in that, data acquisition module connects acceleration sensor and angular velocity Sensors, data acquisition module integrated GPS module. the

Further, the acceleration sensor and the angular velocity sensor are digitally sampled through an AD conversion chip. the

Further, the data acquisition module performs initial synchronization with GPS data through a single-chip microcomputer. the

Further, the data acquisition module transmits information to the ARM embedded system in the form of USB format frames through the USB transmission chip. the

The present invention simultaneously discloses a vehicle weight measurement method based on GPS and inertial sensors, comprising the following steps:

Step 1, the ARM embedded system preprocesses the acceleration and angular velocity: noise reduction-down-sampling-zero drift correction. The mean filtering method is used to suppress the noise of the original lateral acceleration signal and angular velocity signal. Periodically take stable acceleration and angular velocity signals for 5 seconds, calculate the average value for their respective zero drift, and correct the acceleration data and angular velocity data in real time;

Step 2, the ARM embedded system preprocesses the GPS data: first, differentially processes the heading angle in the GPS message to obtain the yaw rate; then, matches the differential result with the angular velocity, and uses the correlation method to obtain the GPS Latency estimation of ; Finally, GPS data is synchronized with acceleration data;

Step 3, use the vehicle speed and yaw rate in the GPS message to obtain the lateral acceleration, and perform statistical fitting on the acceleration in real time to obtain the roll gain.

in, is the lateral acceleration of the car turning, Output vehicle speed for GPS, is the yaw rate of the car, which is obtained from the heading angle difference output by the GPS.

when When it is greater than 0.1g, the obtained acceleration sensor data will be processed and Store, and perform recursive least squares fitting according to the following formula:

in, is the roll gain of the vehicle suspension;

Step 4: Carry out driving experiments with two groups of loads to obtain different roll gains, and match parameters a and b according to the following formula: , where K is the roll gain, m is the weight of the vehicle, which is the total weight after loading, and a and b are undetermined parameters;

Step 5, enter the values of K, a and b to estimate the vehicle weight. the

Compared with prior art, beneficial effect is:

1) The measurement system in the present invention is based on an embedded system, which integrates GPS, acceleration sensor and angular velocity sensor, and can detect vehicle load online in real time.

2) The measurement system in the present invention only needs to be installed and fixed in the car compartment, and does not need to intervene in the car's own system, so it is easy to install. the

3) In the present invention, the GPS heading angle data and the angular velocity sensor data are used for processing and matching, which can estimate the GPS delay in real time and use it for real-time correction. the

4) The present invention uses the characteristics of different rolls of vehicles under different loads to estimate the vehicle weight. The roll gain value is estimated by recursive least squares fitting based on statistical data, and the vehicle weight is estimated according to the linear equation of roll gain and vehicle weight, which has good reliability and robustness. the

Description of drawings

Fig. 1 is a structural representation of the present invention;

Figure 2 is a schematic diagram of the installation location of the measurement system;

Fig. 3 is the flowchart of GPS delay estimation algorithm;

4 is a flowchart of a vehicle roll gain estimation algorithm. the

Detailed ways

The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. the

A measurement system integrating GPS, a three-axis acceleration sensor and an angular velocity sensor and an embedded platform is fixed at a location near the rear axle of the monitored vehicle. The GPS sampling rate is 1Hz, the acceleration range is above ±1g, the angular velocity is ±300°/s, and the sampling rate is 1000Hz. the

After starting up, the monitoring program can run automatically. the

1. Parameter calibration mode:

For a specific vehicle model, parameter calibration is required first.

After installing and fixing the monitoring system, drive on the city road with an empty car for 20 minutes, and the driving process should include multiple turns. The program completes the sensor self-calibration and GPS delay. Calibration is shown in the algorithm in Figure 3, and the roll gain K ₁ of the empty vehicle is calculated (as shown in the algorithm in Figure 4) and saved with the input of the current vehicle weight m ₁ .

Load a load of mass m ₂ and drive on an urban road for 20 minutes. There should be multiple turns during the driving process. The program counts the roll gain K ₂ according to the algorithm (shown in Figure 4), and inputs the current vehicle weight m ₂ to save.

After completing two calibration experiments, complete the parameters according to the following roll gain-vehicle weight equation , calibration.

2. Detection mode:

After the calibration of a certain vehicle type is completed, the system can be installed and fixed in the compartment of this type of vehicle, as shown in Figure 2.

Execute the roll gain estimation procedure, as shown in Figure 4. The roll gain K value obtained by real-time statistics is substituted into the roll gain-vehicle weight equation to calculate the current overall mass of the vehicle. the

The above is only an example of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or process transformation made by using the description of the present invention and the contents of the accompanying drawings, or directly or indirectly used in other related technical fields, All are included in the scope of patent protection of the present invention in the same way. the

Claims (1)

1. A vehicle measuring method based on the vehicle weight measurement system of GPS and inertial sensor, wherein the vehicle weight measurement system comprises ARM embedded system and acceleration sensor, angular velocity sensor, and integrated GPS module, it is characterized in that, comprises the following steps: Step 1, the ARM embedded system preprocesses the acceleration and angular velocity: noise reduction - downsampling - zero drift correction; Step 2, the ARM embedded system preprocesses the GPS data: first, differentially processes the heading angle in the GPS message to obtain the yaw rate; then, matches the differential result with the angular velocity, and uses the correlation method to obtain the GPS Latency estimation of ; Finally, GPS data is synchronized with acceleration data; Step 3, using the vehicle speed and yaw rate in the GPS message to obtain the centripetal acceleration a _y , and the output value a _{y_m} of the lateral acceleration sensor, according to a _{y_m} = a _y (1+K) to perform recursive least squares fitting Combined, the roll gain K is obtained; Step 4: Carry out driving experiments with two groups of loads to obtain different roll gains, and match parameters a and b according to the following formula: Where K is the roll gain, m is the weight of the vehicle, which is the total weight after loading, and a and b are undetermined parameters; Step 5, when using, substitute the values of a and b, and estimate the vehicle weight according to the K value measured in step 3.