WO2020019883A1 - Method for dynamically configuring threshold value of emergency call system of car - Google Patents

Method for dynamically configuring threshold value of emergency call system of car Download PDF

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WO2020019883A1
WO2020019883A1 PCT/CN2019/090235 CN2019090235W WO2020019883A1 WO 2020019883 A1 WO2020019883 A1 WO 2020019883A1 CN 2019090235 W CN2019090235 W CN 2019090235W WO 2020019883 A1 WO2020019883 A1 WO 2020019883A1
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threshold
emergency call
acceleration
vehicle speed
call system
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PCT/CN2019/090235
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Chinese (zh)
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陆颖
吕羽竞
叶恒毅
马龙飞
束瑜
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江苏大学
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/15—Vehicle, aircraft or watercraft design
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2117/00—Details relating to the type or aim of the circuit design
    • G06F2117/08—HW-SW co-design, e.g. HW-SW partitioning

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  • the invention relates to the field of automobile passive safety, in particular to a method for dynamically matching a threshold of a car emergency call system threshold.
  • the ignition signal of the airbag is mainly used as a trigger signal source, that is, the accident monitoring module in the system automatically triggers the call for an emergency module once it detects that the airbag is ignited.
  • the reliability is restricted by the airbag system, and for those vehicles that were not originally equipped with this system, the installation of this type of emergency call for assistance requires changes to the airbag system, which has a high technical difficulty and Great security risk.
  • domestic and foreign countries have actively developed emergency call systems triggered by ignition signals of non-airbags. Due to the advantages of convenient acquisition and processing of vehicle body acceleration signals and easy modification of the in-vehicle system, the emergency call system using the vehicle body acceleration signal as the trigger signal source is the current research hotspot.
  • the accident detection algorithm in the accident detection module of the emergency call system using the body acceleration signal as a trigger information source judges whether the accident occurs and the severity of the accident by comparing the acceleration peak value with a preset threshold.
  • a preset threshold In order to improve the sensitivity to collisions, this threshold is often set low, and the probability of false alarms is high.
  • the threshold of a new type of automobile emergency call system usually includes two levels: one is the “threshold threshold", which is used to judge whether an accident occurs, that is, to distinguish traffic accidents from special conditions such as emergency braking and special road driving; the second is “ “Trigger threshold” is used to determine whether the car emergency call system sends out a call for help, that is, to distinguish a minor collision accident from a serious accident.
  • the threshold setting of the emergency call system of an automobile is mainly obtained through actual vehicle tests on roadblocks and bumpy roads. Generally, the peak acceleration value during the test is used as the threshold threshold. A static value is used. When the road conditions change, The static threshold cannot effectively filter the interference signals, and it cannot effectively and accurately determine whether an accident occurs.
  • the International Roughness Index is one of the most widely used indicators in evaluating road performance. This indicator is based on a quarter of the vehicle model. The test vehicle travels on the road at a speed of 80 km / h. The cumulative vertical displacement of the dynamic response system is used as the IRI value over the driving distance.
  • the IRI detection method is mainly to calculate the IRI value by establishing the relationship between the road elevation and the IRI.
  • the measurement equipment is mostly a laser flatness meter or a level gauge, but these methods require a lot of human and material support, and the cost is very high. Adaptability.
  • the current research mainly uses the power spectrum density (PSD) of the acceleration signal of the vehicle body to establish the acceleration and IRI of the vehicle body. connection relation.
  • PSD power spectrum density
  • the correlation model of IRI, vehicle speed, and threshold thresholds in the car emergency call system has not yet been established. Adjust the threshold threshold.
  • the present invention provides a method that can continuously adjust the threshold value according to the road conditions.
  • the method mainly establishes the correlation model between IRI, vehicle speed, and threshold threshold, and then realizes the threshold value of the vehicle emergency call system based on the existing correlation model of acceleration PSD, vehicle speed, and IRI.
  • the threshold threshold can be adjusted according to the acceleration signal and the vehicle speed signal The purpose is to finally improve the anti-jamming performance of the entire car emergency call system.
  • a method for dynamically matching the threshold of a car emergency call system threshold includes the following steps:
  • Step 1 Collect acceleration data and vehicle speed data during the driving process of the vehicle
  • Step 2 Calculate the power spectral density of the acceleration data
  • Step 3 Calculate the corresponding IRI value based on the collected acceleration PSD and vehicle speed through the correlation model of acceleration PSD, vehicle speed, and IRI preset in the car emergency call system, so as to achieve the purpose of road recognition;
  • Step 4 Calculate the threshold threshold based on the calculated IRI value and the collected vehicle speed through the correlation model of IRI, vehicle speed, and threshold threshold preset in the car emergency call system.
  • the current car emergency call system is rarely associated with the external environment.
  • the present invention establishes a correlation model of IRI value, vehicle speed, and threshold threshold, links the road surface with the car emergency call system, and specifically promotes the research of the car emergency call system.
  • the current car emergency call system uses static thresholds and has poor anti-interference performance.
  • the invention adopts a dynamic threshold threshold value, thereby filtering interference signals more effectively and avoiding too many invalid signals from entering the system, which leads to an increase in the running time of the system.
  • signals that are useful for judging collisions are kept as far as possible, avoiding the car emergency call system not triggering or false triggering in the event of a collision, and ultimately improving the operating efficiency of the entire car emergency call system and the accuracy of external call for help.
  • FIG. 1 is a flowchart of dynamically adjusting a threshold of a car emergency call system threshold according to the present invention
  • FIG. 2 is a flowchart of establishing a correlation model of vehicle speed, IRI, and threshold threshold.
  • a method for dynamically matching a threshold of a car emergency call system threshold includes:
  • Step 1 Collect acceleration data and speed data during the driving of the car:
  • Acceleration sensor Z-axis acceleration data is collected through an acceleration sensor, and vehicle speed data is obtained through the CAN bus of the vehicle.
  • the acceleration data in step 1 can be obtained through an acceleration sensor.
  • the model chosen by the acceleration sensor is ADXL377 from Analog Devices. A total of two identical acceleration sensors are required, which are placed directly above the left and right wheels in the car.
  • the collected acceleration signal is input into the terminal of the car emergency call system through the A / D channel.
  • the vehicle speed data in step 1 can be obtained by connecting the CAN interface of the terminal of the emergency call system of the vehicle to the internal bus of the vehicle.
  • Step 2 Calculate the power spectral density of the acceleration data: According to the vehicle acceleration data obtained in step 1, calculate the acceleration PSD through a calculation program preset in the car emergency call system.
  • the algorithm for calculating the power spectral density in step two is: first calculate the autocorrelation function of the signal, and then obtain the Fourier transform of the autocorrelation function to obtain the power spectral density of the acceleration. Specifically, the autocorrelation function of the acceleration signal is calculated using the xcorr function in Matlab, and the power spectral density of the autocorrelation function is calculated using the fft function in Matlab.
  • the Matlab C ++ compiler is used to convert the Matlab language into the C language, so that it can be imported into a terminal of a car emergency call system.
  • Step 3 According to the collected acceleration signal and vehicle speed, the corresponding IRI value is calculated through the correlation model of acceleration power spectral density, vehicle speed and IRI preset in the car emergency call system, so as to achieve the purpose of road recognition.
  • the correlation model of the acceleration power spectral density, the vehicle speed, and the IRI in step 3 adopts a current existing model, and the specific expression of the model is:
  • IRI 0.562 ⁇ X l + 6.471 ⁇ X r -0.1651
  • ⁇ is the correction factor for speed
  • v is the vehicle speed
  • X l is the integrated square root value of the power spectral density of the acceleration data measured by the acceleration sensor placed above the left front wheel of the car
  • X r is the acceleration data measured by the acceleration sensor placed above the right front wheel of the car The integrated square root of the power spectral density.
  • Step 4 Calculate the threshold threshold based on the calculated IRI value and the collected vehicle speed through the correlation model of IRI, vehicle speed, and threshold threshold preset in the car emergency call system.
  • FIG. 2 it is a flowchart of establishing a correlation model of IRI, vehicle speed, and threshold.
  • the correlation model between IRI, vehicle speed, and threshold threshold is established based on the vehicle speed, IRI, and threshold threshold (peak acceleration) data simulated by the vehicle dynamics simulation software CarSim. It is only necessary to build the vehicle motion in CarSim software The simulation model can simulate these data.
  • the vehicle motion simulation model is composed of a road model and a vehicle model. After the two models are established, the simulation can be performed.
  • the establishment of the pavement model in CarSim requires three-dimensional pavement coordinates.
  • the three-dimensional pavement coordinates can be gradually expanded from the one-dimensional pavement coordinates, and the one-dimensional pavement coordinates can be simulated by the pavement reconstruction algorithm.
  • the pavement reconstruction algorithm selected by the present invention is the inverse Fourier transform method. Although the algorithm is more complex in programming, its operation efficiency is faster and the operation result is more accurate. Pavement reconstruction data can be used to simulate different levels of pavement elevation data, but these elevation data belong to one-dimensional pavement elevation data.
  • two-dimensional road surface coordinates can be obtained by replacing one-dimensional variables in the inverse Fourier transform with two-dimensional variables.
  • the first is a road horizontal linear file, which is a table.
  • the first and second columns of the table are the x and y coordinates corresponding to the centerline coordinates of the road surface.
  • the second is a vertical linear file. Enter the x-coordinates of the centerline and their corresponding z-coordinates in the column and the second column; the third is the pavement unevenness file.
  • the first column of the table is the x-axis coordinates corresponding to the centerline.
  • y-axis coordinate the rest of the table is the z-axis coordinate of each coordinate point of the entire road surface.
  • the position of each coordinate point's z-axis arrangement must correspond to the x-axis coordinate and y-axis coordinate of the coordinate point.
  • CarSim software can generate a three-dimensional virtual pavement.
  • Acceleration data during vehicle driving can be output after running a vehicle motion simulation model.
  • the IRI value needs to be calculated from the pavement elevation.
  • the pavement elevation has been calculated using the pavement reconstruction algorithm. It can also be output after running the vehicle motion simulation model.
  • Based on the pavement elevation data the 45th document of The World International Road Roughness issued by the World Bank in 1986 is used.
  • the IRI calculation method specified in "Experiment Standard for Measurement" calculates the IRI value corresponding to the three-dimensional virtual road surface constructed.
  • the vehicle speed can be set before the simulation runs.
  • the acceleration peak value during the driving of the vehicle is obtained, and this peak value is used as the threshold value.
  • the obtained threshold value, IRI value, and vehicle speed are imported into the SPSS software.
  • the threshold value is used as the dependent variable, and the IRI value and the vehicle speed are used as independent variables.
  • a regression equation between the three is established, and this regression equation is input into the terminal of the car emergency call system.
  • the resulting association model is:
  • v the vehicle speed
  • the correlation model between the threshold and the vehicle speed and IRI is a piecewise function, because the data obtained after the vehicle motion simulation is performed on the A-level road (IRI ⁇ 2.433m / km), and the obtained regression The significance of the equation is poor. Therefore, it is necessary to consider the threshold value of the A-level pavement separately. In fact, when the car is driving on the A-level road, setting the threshold to 2g can filter most of the interference signals, so 2g is selected as the threshold threshold of the A-level road. For other levels of road (IRI ⁇ 2.433m / km), and the threshold setting can be calculated by the correlation model obtained by regression.
  • the corresponding vehicle emergency call system threshold threshold can be calculated based on the Z-direction acceleration and vehicle speed.
  • the car emergency call system can adjust the threshold value of the emergency call system in real time according to the vehicle speed signal and the Z-direction acceleration data measured by the acceleration sensor, so as to improve the anti-interference performance of the system.
  • the "real-time adjustment" mentioned here is not to adjust the threshold threshold once when the acceleration sensor collects an acceleration signal, that is, the frequency of adjusting the threshold threshold cannot be the same as the frequency of the signal collected by the acceleration sensor.
  • the present invention provides that the threshold threshold is automatically adjusted once every 500 meters of the vehicle.

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Abstract

Disclosed is a method for dynamically configuring a threshold value of an emergency call system of a car. The specific solution comprises: step 1, acquiring acceleration data and car speed data while a car is traveling; step 2, calculating a power spectral density of the acceleration data; step 3, calculating, by means of a preset correlation model of the power spectral density of the acceleration, the car speed, and the international roughness index in the emergency call system of the car, a corresponding international roughness index value according to the acquired power spectral density of the acceleration and the car speed, thereby achieving road surface identification; and step 4, calculating, by means of a preset correlation model of the international roughness index, the car speed, and a threshold value in the emergency call system of the car, a threshold value according to the calculated international roughness index value and the acquired car speed. The present invention adopts a dynamic threshold value, thereby effectively filtering out interference signals, and improving the operational efficiency and the accuracy of making an outbound call for the entire car emergency call system.

Description

一种汽车紧急呼救***门槛阈值动态匹配方法Dynamic matching method for threshold of automobile emergency call system 技术领域Technical field
本发明涉及汽车被动安全领域，尤其涉及一种汽车紧急呼救***门槛阈值动态匹配的方法。The invention relates to the field of automobile passive safety, in particular to a method for dynamically matching a threshold of a car emergency call system threshold.
背景技术Background technique
随着中国汽车保有量的增加，交通事故由单一性向复杂性、严重性发展，由两车碰撞向多车碰撞方向发展。重大交通事故的发生，给人民的生命财产带来严重损失，所以，对汽车紧急呼救***的研究十分必要。With the increase in the number of cars in China, traffic accidents have developed from a singularity to complexity and severity, and from a two-vehicle collision to a multi-vehicle collision. The occurrence of major traffic accidents has caused serious losses to people's lives and property. Therefore, research on the emergency call system for automobiles is very necessary.
现有的汽车紧急呼救***，主要是将安全气囊点火信号作为触发信号源，即***中的事故监测模块一旦检测到安全气囊发生点火，就自动触发呼救模块。但是，其可靠性受到安全气囊***的制约，且对于那些原本未装备该***的车辆来说，要想加装这种类型的紧急呼救***需要对安全气囊***更改，具有很高的技术难度和极大的安全风险。基于此，近年来国内外积极研发非安全气囊点火信号触发的紧急呼救***。由于车身加速度信号具有采集、处理方便，车内***改装容易的优势，因此以车身加速度信号作为触发信号源的紧急呼救***是目前研究的热点。In an existing emergency call system for an automobile, the ignition signal of the airbag is mainly used as a trigger signal source, that is, the accident monitoring module in the system automatically triggers the call for an emergency module once it detects that the airbag is ignited. However, its reliability is restricted by the airbag system, and for those vehicles that were not originally equipped with this system, the installation of this type of emergency call for assistance requires changes to the airbag system, which has a high technical difficulty and Great security risk. Based on this, in recent years, domestic and foreign countries have actively developed emergency call systems triggered by ignition signals of non-airbags. Due to the advantages of convenient acquisition and processing of vehicle body acceleration signals and easy modification of the in-vehicle system, the emergency call system using the vehicle body acceleration signal as the trigger signal source is the current research hotspot.
以车身加速度信号作为触发信息源的紧急呼救***其事故检测模块中的事故检测算法通过比较加速度峰值与事先设置的阈值来判断事故是否发生以及事故的严重程度。但是，这类呼救***一般都采用单一的阈值，而且为了提高对碰撞的敏感性，这种阈值往往设置得较低，出现误报警的概率很大。因而，新型的汽车紧急呼救***，其阈值通常包括两个层次：一是“门槛阈值”，用来判断事故是否发生，即区分交通事故与紧急刹车、特殊路面行驶等特殊工况；二是“触发阈值”，用来决定汽车紧急呼救***是否对外发出呼救信号，即区分轻微碰撞事故与严重事故。汽车紧急呼救***门槛阈值的设定主要是通过路障和颠簸路面的实车试验得到，一般是以试验过程中的加速度峰值作为门槛阈值，采用的是一个静态的数值，当路面情况发生变化时，静态的门槛阈值无法有效地过滤干扰信号，也就无法有效准确地判定事故是否发生。The accident detection algorithm in the accident detection module of the emergency call system using the body acceleration signal as a trigger information source judges whether the accident occurs and the severity of the accident by comparing the acceleration peak value with a preset threshold. However, such call-for-help systems generally use a single threshold, and in order to improve the sensitivity to collisions, this threshold is often set low, and the probability of false alarms is high. Therefore, the threshold of a new type of automobile emergency call system usually includes two levels: one is the "threshold threshold", which is used to judge whether an accident occurs, that is, to distinguish traffic accidents from special conditions such as emergency braking and special road driving; the second is " "Trigger threshold" is used to determine whether the car emergency call system sends out a call for help, that is, to distinguish a minor collision accident from a serious accident. The threshold setting of the emergency call system of an automobile is mainly obtained through actual vehicle tests on roadblocks and bumpy roads. Generally, the peak acceleration value during the test is used as the threshold threshold. A static value is used. When the road conditions change, The static threshold cannot effectively filter the interference signals, and it cannot effectively and accurately determine whether an accident occurs.
国际平整度指数(International Roughness Index，IRI)是评价路面性能方面应用最广泛的指标之一。该指标以四分之一车辆模型为基础，测试车辆以80km/h的速度行驶在路面上，在行驶距离内由动态反应***的累积竖向位移量作为IRI值。IRI的检测方法主要是通过建立路面高程与IRI的关系来计算出IRI值，测量设备多为激光式平整度仪或者是水准仪，但是这些方法需要大量的人力物力支撑，成本很高，不具备普适性。由于车身加速度信号只需要加速度传感器就可以获得，而且加速度传感器的成本也比较低，因此现有的研究主要是通过车身加速度信号的功率谱密度(Power Spectrum Density，PSD)来建立车身加速度与IRI的关联关系。The International Roughness Index (IRI) is one of the most widely used indicators in evaluating road performance. This indicator is based on a quarter of the vehicle model. The test vehicle travels on the road at a speed of 80 km / h. The cumulative vertical displacement of the dynamic response system is used as the IRI value over the driving distance. The IRI detection method is mainly to calculate the IRI value by establishing the relationship between the road elevation and the IRI. The measurement equipment is mostly a laser flatness meter or a level gauge, but these methods require a lot of human and material support, and the cost is very high. Adaptability. Because the acceleration signal of the vehicle body can be obtained only by the acceleration sensor, and the cost of the acceleration sensor is relatively low, the current research mainly uses the power spectrum density (PSD) of the acceleration signal of the vehicle body to establish the acceleration and IRI of the vehicle body. connection relation.
综上所述，目前的研究虽然已经将车身加速度PSD、车速、IRI进行关联，但仍未建立IRI、车速与汽车紧急呼救***中的门槛阈值的关联模型，因此，无法根据路面情况与车速实时调整门槛阈值。In summary, although the current research has linked the body acceleration PSD, vehicle speed, and IRI, the correlation model of IRI, vehicle speed, and threshold thresholds in the car emergency call system has not yet been established. Adjust the threshold threshold.
发明内容Summary of the Invention
针对以上问题，本发明提供了一种可根据路面情况不断调整门槛阈值的方法。该方法主要通过建立IRI、车速、门槛阈值之间的关联模型，再根据已有的加速度PSD、车速、IRI的关联模型，实现汽车紧急呼救***的门槛阈值可根据加速度信号和车速信号调整门槛阈值的目的，最终提高整个汽车紧急呼救***的抗干扰性能。In view of the above problems, the present invention provides a method that can continuously adjust the threshold value according to the road conditions. The method mainly establishes the correlation model between IRI, vehicle speed, and threshold threshold, and then realizes the threshold value of the vehicle emergency call system based on the existing correlation model of acceleration PSD, vehicle speed, and IRI. The threshold threshold can be adjusted according to the acceleration signal and the vehicle speed signal The purpose is to finally improve the anti-jamming performance of the entire car emergency call system.
本发明具体采用如下技术方案：The present invention specifically adopts the following technical solutions:
一种汽车紧急呼救***门槛阈值动态匹配方法，包括如下步骤：A method for dynamically matching the threshold of a car emergency call system threshold includes the following steps:
步骤一、采集汽车行驶过程中的加速度数据与车速数据；Step 1: Collect acceleration data and vehicle speed data during the driving process of the vehicle;
步骤二、计算加速度数据的功率谱密度；Step 2: Calculate the power spectral density of the acceleration data;
步骤三、通过预置在汽车紧急呼救***中的加速度PSD、车速与IRI的关联模型，根据采集到的加速度PSD与车速计算出相应的IRI值，从而达到路面识别的目的；Step 3: Calculate the corresponding IRI value based on the collected acceleration PSD and vehicle speed through the correlation model of acceleration PSD, vehicle speed, and IRI preset in the car emergency call system, so as to achieve the purpose of road recognition;
步骤四、通过预置在汽车紧急呼救***中的IRI、车速与门槛阈值的关联模型，根据计算得到的IRI值及采集到的车速计算出门槛阈值。Step 4: Calculate the threshold threshold based on the calculated IRI value and the collected vehicle speed through the correlation model of IRI, vehicle speed, and threshold threshold preset in the car emergency call system.
本发明的有益效果是：The beneficial effects of the present invention are:
目前的汽车紧急呼救***很少与外界环境进行关联，本发明建立了IRI值、车速、门槛阈值的关联模型，将路面与汽车紧急呼救***联系起来，对汽车紧急呼救***的研究具体推动意义。The current car emergency call system is rarely associated with the external environment. The present invention establishes a correlation model of IRI value, vehicle speed, and threshold threshold, links the road surface with the car emergency call system, and specifically promotes the research of the car emergency call system.
目前的汽车紧急呼救***采用的都是静态的门槛阈值，抗干扰性较差。本发明采用动态门槛阈值，从而更有效地过滤干扰信号，避免过多无效信号进入***，导致***的运行时间增加。同时，也尽可能地保留了对判断碰撞有用的信号，避免汽车紧急呼救***在发生碰撞时不触发或误触发，最终提高整个汽车紧急呼救***的运行效率和对外呼救的准确性。The current car emergency call system uses static thresholds and has poor anti-interference performance. The invention adopts a dynamic threshold threshold value, thereby filtering interference signals more effectively and avoiding too many invalid signals from entering the system, which leads to an increase in the running time of the system. At the same time, signals that are useful for judging collisions are kept as far as possible, avoiding the car emergency call system not triggering or false triggering in the event of a collision, and ultimately improving the operating efficiency of the entire car emergency call system and the accuracy of external call for help.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
图1是本发明所述的汽车紧急呼救***门槛阈值动态调整的流程图；FIG. 1 is a flowchart of dynamically adjusting a threshold of a car emergency call system threshold according to the present invention;
图2是建立车速、IRI与门槛阈值的关联模型的流程图。FIG. 2 is a flowchart of establishing a correlation model of vehicle speed, IRI, and threshold threshold.
具体实施方式detailed description
下面结合附图对本发明的技术方法做进一步的详细说明：The technical method of the present invention is described in further detail below with reference to the drawings:
如图1所示，本发明所述的一种汽车紧急呼救***门槛阈值动态匹配方法，包括：As shown in FIG. 1, a method for dynamically matching a threshold of a car emergency call system threshold according to the present invention includes:
步骤一、采集汽车行驶过程中的加速度数据与车速数据：Step 1: Collect acceleration data and speed data during the driving of the car:
通过加速度传感器采集汽车Z向加速度数据，通过汽车的CAN总线获得汽车的车速数据。Acceleration sensor Z-axis acceleration data is collected through an acceleration sensor, and vehicle speed data is obtained through the CAN bus of the vehicle.
步骤一中的加速度数据可通过加速度传感器获得，加速度传感器选用的型号是ADI公司的ADXL377，总共需要两个完全相同的加速度传感器，分别放置于车内左、右车轮的正上方。采集到的加速度信号通过A/D通道输入进汽车紧急呼救***的终端中。步骤一中的车速数据可通过将汽车紧急呼救***终端的CAN接口接入车辆内部总线获得。The acceleration data in step 1 can be obtained through an acceleration sensor. The model chosen by the acceleration sensor is ADXL377 from Analog Devices. A total of two identical acceleration sensors are required, which are placed directly above the left and right wheels in the car. The collected acceleration signal is input into the terminal of the car emergency call system through the A / D channel. The vehicle speed data in step 1 can be obtained by connecting the CAN interface of the terminal of the emergency call system of the vehicle to the internal bus of the vehicle.
步骤二、计算加速度数据的功率谱密度:根据步骤一中得到的车身加速度数据，通过预置在汽车紧急呼救***中的计算程序计算出加速度PSD。Step 2: Calculate the power spectral density of the acceleration data: According to the vehicle acceleration data obtained in step 1, calculate the acceleration PSD through a calculation program preset in the car emergency call system.
进一步地，步骤二中计算功率谱密度的算法为：先计算信号的自相关函数，再求出自相关函数的傅里叶变换即可得到加速度的功率谱密度。具体地，利用Matlab中的xcorr函数计算加速度信号的自相关函数，利用Matlab中的fft函数计算出自相关函数的功率谱密度。Further, the algorithm for calculating the power spectral density in step two is: first calculate the autocorrelation function of the signal, and then obtain the Fourier transform of the autocorrelation function to obtain the power spectral density of the acceleration. Specifically, the autocorrelation function of the acceleration signal is calculated using the xcorr function in Matlab, and the power spectral density of the autocorrelation function is calculated using the fft function in Matlab.
进一步地，在Matlab中将计算加速度功率谱密度的程序编写完成之后，利用Matlab的C++编译器将Matlab语言转化成C语言，从而能够将其导入汽车紧急呼救***的终端中。Further, after the program for calculating the acceleration power spectral density is completed in Matlab, the Matlab C ++ compiler is used to convert the Matlab language into the C language, so that it can be imported into a terminal of a car emergency call system.
步骤三、根据采集到的加速度信号与车速，通过预置在汽车紧急呼救***中的加速度功率谱密度、车速与IRI的关联模型计算出相应的IRI值，从而达到路面识别的目的。Step 3: According to the collected acceleration signal and vehicle speed, the corresponding IRI value is calculated through the correlation model of acceleration power spectral density, vehicle speed and IRI preset in the car emergency call system, so as to achieve the purpose of road recognition.
进一步地，步骤三中加速度功率谱密度、车速与IRI的关联模型，采用目前已有的模型，该模型的具体表达式为：Further, the correlation model of the acceleration power spectral density, the vehicle speed, and the IRI in step 3 adopts a current existing model, and the specific expression of the model is:
IRI＝0.562αX l+6.471αX r-0.1651 IRI = 0.562αX l + 6.471αX r -0.1651
式中，α为速度的修正系数，且
Figure PCTCN2019090235-appb-000001
v为车速，X l为放置于汽车左前轮上方的加速度传感器测得的加速度数据的功率谱密度的积分开方值，X r为放置于汽车右前轮上方的加速度传感器测得的加速度数据的功率谱密度的积分开方值。 Where α is the correction factor for speed, and
Figure PCTCN2019090235-appb-000001
v is the vehicle speed, X l is the integrated square root value of the power spectral density of the acceleration data measured by the acceleration sensor placed above the left front wheel of the car, and X r is the acceleration data measured by the acceleration sensor placed above the right front wheel of the car The integrated square root of the power spectral density.
步骤四、通过预置在汽车紧急呼救***中的IRI、车速与门槛阈值的关联模型，根据计算得到的IRI值及采集到车速计算出门槛阈值。Step 4: Calculate the threshold threshold based on the calculated IRI value and the collected vehicle speed through the correlation model of IRI, vehicle speed, and threshold threshold preset in the car emergency call system.
如图2所示，是建立IRI、车速、门槛阈值的关联模型的流程图。As shown in FIG. 2, it is a flowchart of establishing a correlation model of IRI, vehicle speed, and threshold.
进一步地，IRI、车速、门槛阈值三者之间的关联模型是依据汽车动力学仿真软件CarSim模拟出的车速、IRI、门槛阈值(加速度峰值)数据建立的，只需要在CarSim软件中构建车辆运动仿真模型即可模拟出这些数据。车辆运动仿真模型由路面模型和车辆模型两部分组成，将这两个模型建立完成，即可进行仿真。Further, the correlation model between IRI, vehicle speed, and threshold threshold is established based on the vehicle speed, IRI, and threshold threshold (peak acceleration) data simulated by the vehicle dynamics simulation software CarSim. It is only necessary to build the vehicle motion in CarSim software The simulation model can simulate these data. The vehicle motion simulation model is composed of a road model and a vehicle model. After the two models are established, the simulation can be performed.
CarSim中路面模型的建立需要三维路面坐标，三维路面坐标可由一维路面坐标逐步拓展得到，而一维路面坐标可由路面重构算法模拟得到。本发明选用的路面重构算法为傅里叶逆变换法，该算法虽然编程较为复杂，但是其运算效率较快，而且运算结果也比较精确。通过路面重构算法可以模拟出不同等级的路面高程数据，但这些高程数据属于一维路面高程数据。The establishment of the pavement model in CarSim requires three-dimensional pavement coordinates. The three-dimensional pavement coordinates can be gradually expanded from the one-dimensional pavement coordinates, and the one-dimensional pavement coordinates can be simulated by the pavement reconstruction algorithm. The pavement reconstruction algorithm selected by the present invention is the inverse Fourier transform method. Although the algorithm is more complex in programming, its operation efficiency is faster and the operation result is more accurate. Pavement reconstruction data can be used to simulate different levels of pavement elevation data, but these elevation data belong to one-dimensional pavement elevation data.
在已有一维路面高程数据的情况下，将傅里叶逆变换中的一维变量用二维变量代替即可得到二维路面坐标。In the case of existing one-dimensional road surface elevation data, two-dimensional road surface coordinates can be obtained by replacing one-dimensional variables in the inverse Fourier transform with two-dimensional variables.
为了将二维路面坐标拓展成CarSim软件所需要的三维路面坐标，只需要将二维路面坐标所用的(x,y,z)坐标系转化为适用于CarSim软件的(S,L,Z)坐标系即可。当建立的路面为弯道路面时，这种坐标系的转化比较复杂，但是建立的路面是平直路面时，(x,y,z)坐标系与(S,L,Z)坐标系完全相同，所以不需要转化。本发明在构建关联模型时所建立的路面就是平直路面，所以在构建三维虚拟路面时所用到的三维路面坐标就是二维路面坐标。In order to expand the 2D pavement coordinates into the 3D pavement coordinates required by CarSim software, it is only necessary to convert the (x, y, z) coordinate system used by the 2D pavement coordinates into (S, L, Z) coordinates suitable for CarSim software. Yes. When the established road surface is a curved road surface, the transformation of this coordinate system is more complicated, but when the established road surface is a straight road surface, the (x, y, z) coordinate system is exactly the same as the (S, L, Z) coordinate system. , So no conversion is needed. The road surface established by the present invention when constructing the association model is a straight road surface, so the three-dimensional road surface coordinates used in constructing the three-dimensional virtual road surface are two-dimensional road surface coordinates.
在得到二维路面坐标之后，需要将其导入CarSim软件中的三个路面文件。第一个是道路水平线形文件，也就是一个表格，表格的第一列和第二列输入路面的中心线坐标对应的x坐标和y坐标；第二个是纵断面线形文件，表格的第一列和第二列输入中心线的x坐标及其对应的z坐标；第三个是路面不平度文件，表格的第一列为中心线对应的x轴坐标，第一行为整个路面各个坐标点的y轴坐标，表格的剩余部分为整个路面各个坐标点的z轴坐标，同时，每个坐标点z轴坐标排列的位置必须与该坐标点的x轴坐标和y轴坐标对应。After getting the 2D pavement coordinates, you need to import them into the three pavement files in CarSim software. The first is a road horizontal linear file, which is a table. The first and second columns of the table are the x and y coordinates corresponding to the centerline coordinates of the road surface. The second is a vertical linear file. Enter the x-coordinates of the centerline and their corresponding z-coordinates in the column and the second column; the third is the pavement unevenness file. The first column of the table is the x-axis coordinates corresponding to the centerline. y-axis coordinate, the rest of the table is the z-axis coordinate of each coordinate point of the entire road surface. At the same time, the position of each coordinate point's z-axis arrangement must correspond to the x-axis coordinate and y-axis coordinate of the coordinate point.
进一步地，将二维路面坐标导入三个路面文件之后，CarSim软件即可生成三维虚拟路面。Further, after importing the two-dimensional pavement coordinates into three pavement files, CarSim software can generate a three-dimensional virtual pavement.
构建车辆运动仿真模型除了需要构建三维虚拟路面，还需要构建完整的车辆模型。在CarSim中自带不同等级的车辆模型，所以可直接选用CarSim软件中的车辆模型。在建立路面模型和车辆模型之后，即可进行车辆动力学仿真。In addition to constructing a vehicle motion simulation model, in addition to constructing a three-dimensional virtual road surface, it is also necessary to construct a complete vehicle model. CarSim comes with different levels of vehicle models, so you can directly use the vehicle models in CarSim software. After the road surface model and the vehicle model are established, the vehicle dynamics simulation can be performed.
汽车行驶过程中的加速度数据可以在运行车辆运动仿真模型之后输出。IRI值需要通过路面高程计算，路面高程已利用路面重构算法计算得到，也可以在运行车辆运动仿真模型之后输出，根据路面高程数据，运用1986年世界银行发布的45号文件《The International Road Roughness Experiment Standard for Measurement》中规定的IRI计算方法来计算所构建的三维虚拟路面对应的IRI值。车速可以在仿真运行之前设置。Acceleration data during vehicle driving can be output after running a vehicle motion simulation model. The IRI value needs to be calculated from the pavement elevation. The pavement elevation has been calculated using the pavement reconstruction algorithm. It can also be output after running the vehicle motion simulation model. Based on the pavement elevation data, the 45th document of The World International Road Roughness issued by the World Bank in 1986 is used. The IRI calculation method specified in "Experiment Standard for Measurement" calculates the IRI value corresponding to the three-dimensional virtual road surface constructed. The vehicle speed can be set before the simulation runs.
进一步地，求出汽车行驶过程中加速度峰值，将此峰值作为门槛阈值。将得到门槛阈值、IRI值与车速导入SPSS软件中，将门槛阈值作为因变量，IRI值和车速作为自变量，建立三者之间的回归方程，将此回归方程输入汽车紧急呼救***终端中，作为门槛阈值、车速与IRI值的关联模型。得到的关联模型为：Further, the acceleration peak value during the driving of the vehicle is obtained, and this peak value is used as the threshold value. The obtained threshold value, IRI value, and vehicle speed are imported into the SPSS software. The threshold value is used as the dependent variable, and the IRI value and the vehicle speed are used as independent variables. A regression equation between the three is established, and this regression equation is input into the terminal of the car emergency call system. As the correlation model of threshold threshold, vehicle speed and IRI value. The resulting association model is:
Figure PCTCN2019090235-appb-000002
Figure PCTCN2019090235-appb-000002
式中，v表示汽车车速。In the formula, v represents the vehicle speed.
式中，门槛阈值与车速和IRI的关联模型之所以是一个分段函数，是因为对在A级路面(IRI<2.433m/km)上进行车辆运动仿真后得到的数据进行回归，得到的回归方程的显著性较差。所以，需要单独考虑A级路面的门槛阈值。事实上，当汽车在A级路面上行驶时，将阈值设为2g已经可以过滤大部分的干扰信号，所以，选择2g作为A级路面的门槛阈值，对于其他等级的路面(IRI≥2.433m/km)，门槛阈值的设定可通过回归得到的关联模型计算得到。In the formula, the correlation model between the threshold and the vehicle speed and IRI is a piecewise function, because the data obtained after the vehicle motion simulation is performed on the A-level road (IRI <2.433m / km), and the obtained regression The significance of the equation is poor. Therefore, it is necessary to consider the threshold value of the A-level pavement separately. In fact, when the car is driving on the A-level road, setting the threshold to 2g can filter most of the interference signals, so 2g is selected as the threshold threshold of the A-level road. For other levels of road (IRI≥2.433m / km), and the threshold setting can be calculated by the correlation model obtained by regression.
进一步地，将IRI与加速度PSD、车速之间的关联模型代入门槛阈值与IRI、车速之间的关联模型中，即可根据汽车Z向加速度与车速计算出对应的汽车紧急呼救***的门槛阈值。Further, by substituting the correlation model between IRI, acceleration PSD, and vehicle speed into the correlation model between threshold threshold and IRI, vehicle speed, the corresponding vehicle emergency call system threshold threshold can be calculated based on the Z-direction acceleration and vehicle speed.
通过以上步骤，建立了Z向加速度与汽车速度—IRI—门槛阈值之间的关联关系。汽车紧急呼救***可根据车速信号与加速度传感器测得的Z向加速度数据实时调整紧急呼救***的门槛阈值，从而达到提高***抗干扰性能的目的。但需要注意的是，这里所说的“实时调整”并不是加速度传感器采集到一个加速度信号就调整一次门槛阈值，也就是说调整门槛阈值的频率不能与加速度传感器采集信号的频率一致，而是要远远小于加速度传感器采集信号的频率，因为当这两种频率一致时，也就是紧急呼救***的门槛阈值变化过快，反而无法准确过滤加速度信号即无法准确区分交通事故与紧急刹车、特殊路面行驶等特殊工况。为了使得用来调整加速度峰值(门槛阈值)的加速度信号足够多，本发明规定汽车每行驶500m，自动调整门槛阈值一次。Through the above steps, the correlation between the acceleration in the Z direction and the vehicle speed-IRI-threshold threshold is established. The car emergency call system can adjust the threshold value of the emergency call system in real time according to the vehicle speed signal and the Z-direction acceleration data measured by the acceleration sensor, so as to improve the anti-interference performance of the system. However, it should be noted that the "real-time adjustment" mentioned here is not to adjust the threshold threshold once when the acceleration sensor collects an acceleration signal, that is, the frequency of adjusting the threshold threshold cannot be the same as the frequency of the signal collected by the acceleration sensor. It is much smaller than the frequency of the signal collected by the acceleration sensor, because when the two frequencies are the same, that is, the threshold of the emergency call system changes too quickly, but the acceleration signal cannot be accurately filtered, that is, the traffic accident cannot be accurately distinguished from emergency braking and special road driving. And other special conditions. In order to make enough acceleration signals for adjusting the acceleration peak value (threshold threshold), the present invention provides that the threshold threshold is automatically adjusted once every 500 meters of the vehicle.

Claims (4)

  1. 一种汽车紧急呼救***门槛阈值动态匹配方法，其特征在于包括如下步骤：A method for dynamically matching the threshold value of a car emergency call system, which is characterized by including the following steps:
    步骤一、采集汽车行驶过程中的加速度数据与车速数据；Step 1: Collect acceleration data and vehicle speed data during the driving process of the vehicle;
    步骤二、计算加速度数据的功率谱密度；Step 2: Calculate the power spectral density of the acceleration data;
    步骤三、通过预置在汽车紧急呼救***中的加速度功率谱密度、车速与国际平整度指数的关联模型，根据采集到的加速度功率谱密度与车速计算出相应的国际平整度指数值；Step 3: Calculate the corresponding international flatness index value based on the acceleration power spectral density, vehicle speed, and international flatness index preset in the car emergency call system based on the collected acceleration power spectral density and vehicle speed;
    步骤四、通过预置在汽车紧急呼救***中的国际平整度指数、车速与门槛阈值的关联模型，根据计算得到的国际平整度指数值及采集到的车速计算出门槛阈值，进行动态调整。Step 4: Calculate the threshold threshold based on the correlation model of the international flatness index, vehicle speed, and threshold threshold preset in the car emergency call system, and dynamically adjust the threshold threshold based on the calculated international flatness index value and the collected vehicle speed.
  2. 如权利要求1所述的汽车紧急呼救***门槛阈值动态匹配方法，其特征在于国际平整度指数、车速与门槛阈值的关联模型是将门槛阈值作为因变量，国际平整度指数值和车速作为自变量，建立三者之间的回归方程，得到的回归方程为：The method for dynamically matching threshold thresholds of an automobile emergency call system according to claim 1, wherein the correlation model of the international flatness index, vehicle speed and threshold threshold is to use the threshold threshold as the dependent variable, and the international flatness index value and the vehicle speed as independent variables , Establish a regression equation between the three, and get the regression equation:
    Figure PCTCN2019090235-appb-100001
    Figure PCTCN2019090235-appb-100001
    其中v为车速，IRI为国际平整度指数。Where v is the vehicle speed and IRI is the international flatness index.
  3. 如权利要求1所述的汽车紧急呼救***门槛阈值动态匹配方法，其特征加速度的功率谱密度、车速与国际平整度指数的关联模型，其回归方程如下：The method for dynamically matching the threshold of a car emergency call system threshold according to claim 1, wherein the correlation model of the power spectral density of the characteristic acceleration, the vehicle speed, and the international flatness index is as follows:
    IRI＝0.562αX l+6.471αX r-0.1651， IRI = 0.562αX l + 6.471αX r -0.1651,
    式中，α为速度的修正系数，且
    Figure PCTCN2019090235-appb-100002
    v为车速，X l为汽车左前轮的加速度数据的功率谱密度的积分开方值，X r为汽车右前轮的加速度数据的功率谱密度的积分开方值。     Where α is the correction factor for speed, and
    Figure PCTCN2019090235-appb-100002
    v is the vehicle speed, X l is the integrated square root value of the power spectral density of the acceleration data of the left front wheel of the car, and X r is the square root value of the power spectral density of the acceleration data of the right front wheel of the vehicle.
  4. 如权利要求1所述的汽车紧急呼救***门槛阈值动态匹配方法，其特征在于动态调整门槛阈值的频率远远小于加速度传感器采集信号的频率，以准确过滤加速度信号。The method for dynamically matching the threshold value of a car emergency call system according to claim 1, wherein the frequency of dynamically adjusting the threshold value is much lower than the frequency of the signal collected by the acceleration sensor to accurately filter the acceleration signal.
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