CN109118786B - Vehicle speed prediction method based on quantization adaptive Kalman filtering - Google Patents

Vehicle speed prediction method based on quantization adaptive Kalman filtering Download PDF

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CN109118786B
CN109118786B CN201810947025.0A CN201810947025A CN109118786B CN 109118786 B CN109118786 B CN 109118786B CN 201810947025 A CN201810947025 A CN 201810947025A CN 109118786 B CN109118786 B CN 109118786B
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CN109118786A (en
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钱丽萍
冯安琪
黄玉蘋
冯旭
黄亮
吴远
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Zhejiang University of Technology ZJUT
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Abstract

A vehicle speed prediction method based on quantization adaptive Kalman filtering comprises the following steps: firstly, in an intelligent network traffic system, automatically identifying running vehicles and acquiring related data through a DSRC technology, so as to realize information interaction between a vehicle-mounted system and a roadside unit; secondly, quantizing the azimuth angles of the roadside unit and the vehicle-mounted system by a quantization formula according to the collected related information, predicting the acceleration by an autoregressive moving average method, and finally predicting the speed by using adaptive Kalman filtering to reach a speed correction value; and finally, transmitting the processed information to other 3 roadside units through an optical cable so as to facilitate information interaction with the vehicle-mounted system next time. The invention provides a vehicle speed prediction method based on quantitative adaptive Kalman filtering in an intelligent internet traffic system.

Description

一种基于量化自适应卡尔曼滤波的车辆速度预测方法A Vehicle Speed Prediction Method Based on Quantized Adaptive Kalman Filtering

技术领域technical field

本发明属于交通领域，尤其是涉及智能网联交通系统下的一种基于量化自适应卡尔曼滤波的车辆速度预测方法。The invention belongs to the field of traffic, and in particular relates to a vehicle speed prediction method based on quantized adaptive Kalman filtering under an intelligent networked traffic system.

背景技术Background technique

中国作为世界上人口最多的国家，改革开放以来，随着我国经济的快速发展，人民生活水平的日益提高，私家车开始进入各家各户，很好的方便了家人的出行。但车辆的普及和大众化也使得城市交通环境日益恶化，出现了车流不均衡、交通拥挤、车尾碰撞、侧方碰撞等交通现象。伴随着基础设施薄弱和交通网络拥堵，道路交通事故的数量日益增加，高交通事故发生率正在向全社会拉响警报，因此道路交通安全受到极大的关注。近年来，虽然我国对道路基础设施以及交通网络进行了很大的改善，使得交通事故数量和伤亡人数有所减少，但事故总数和发生率仍然很高。China is the most populous country in the world. Since the reform and opening up, with the rapid development of my country's economy and the improvement of people's living standards, private cars have begun to enter every household, which is very convenient for family travel. However, the popularization and popularization of vehicles have also made the urban traffic environment worsening day by day, and there have been traffic phenomena such as uneven traffic flow, traffic congestion, rear collision, and side collision. With the weak infrastructure and traffic network congestion, the number of road traffic accidents is increasing day by day, and the high traffic accident rate is sounding the alarm to the whole society, so road traffic safety has received great attention. In recent years, although the road infrastructure and transportation network have been greatly improved in China, which has reduced the number of traffic accidents and casualties, the total number and incidence of accidents are still high.

与传统的道路交通系统相比，智能网联交通系统更加趋向于由“人”、“路”、“车”以及公路交通设施等进行信息交互的动态系统。根据各国大量的统计研究后发现，驾驶人的失误是导致交通事故的主要因素。因此，在当前道路基础设施已经不能再完善的情况下，获取道路其他车道车辆的状态信息并加工处理广播给当前车辆的工作刻不容缓，这使得驾驶员能更好的采取相应的补救措施，减小驾驶员因失误导致的交通事故。Compared with the traditional road traffic system, the intelligent networked traffic system tends to be a dynamic system with information interaction between "people", "road", "vehicles" and road traffic facilities. According to a large number of statistical studies in various countries, it is found that the driver's error is the main factor leading to traffic accidents. Therefore, when the current road infrastructure can no longer be perfected, it is urgent to obtain the status information of vehicles in other lanes of the road and process and broadcast it to the current vehicle, which enables drivers to better take corresponding remedial measures to reduce A traffic accident caused by driver error.

发明内容SUMMARY OF THE INVENTION

为了克服现有道路交通系统的安全性较低、交通事故发生概率较高的不足，本发明提供了一种在智能网联交通系统下基于量化自适应卡尔曼滤波的车辆速度预测方法。In order to overcome the shortcomings of low safety and high probability of traffic accidents in the existing road traffic system, the present invention provides a vehicle speed prediction method based on quantitative adaptive Kalman filtering in an intelligent networked traffic system.

本发明解决其技术问题所采用的技术方案是：The technical scheme adopted by the present invention to solve its technical problems is:

一种基于量化自适应卡尔曼滤波的车辆速度预测方法，所述预测方法包括如下步骤：A vehicle speed prediction method based on quantization adaptive Kalman filtering, the prediction method comprises the following steps:

1)在智能网联交通系统中，通过DSRC技术自动识别行驶的车辆并获取相关数据，实现车载系统与路边单元的信息交互，其中，信息交互的步骤如下：1) In the intelligent network-connected transportation system, DSRC technology is used to automatically identify driving vehicles and obtain relevant data, so as to realize the information exchange between the vehicle-mounted system and the roadside unit. The steps of information exchange are as follows:

步骤1.1：当行驶车辆进入定向天线所覆盖的范围内时，车载系统会与路边单元通过DSRC技术实现双向通信，使得双方能同时发送自身存储单元上的信息，其中，车载系统发送的信息包括车辆的当前速度、当前位置和时间戳，路边单元发送的信息包括其他车道上车辆的预测位置、位于哪个方向上、几车道和加速度；Step 1.1: When the driving vehicle enters the range covered by the directional antenna, the on-board system will realize two-way communication with the roadside unit through DSRC technology, so that both parties can send the information on their own storage units at the same time. The information sent by the on-board system includes: The current speed, current position and timestamp of the vehicle, the information sent by the roadside unit includes the predicted position of the vehicle in other lanes, in which direction, how many lanes and acceleration;

步骤1.2：路边单元将获取到的车辆信息发送到边缘云服务器进行一系列的运算操作；Step 1.2: The roadside unit sends the obtained vehicle information to the edge cloud server for a series of computing operations;

2)边缘云服务器根据路边单元与车载系统之间的方位差，进行方位角计算并做相应的量化处理，针对位置信息对车辆行驶方向进行量化，其中，量化过程为：2) According to the azimuth difference between the roadside unit and the vehicle-mounted system, the edge cloud server calculates the azimuth angle and performs corresponding quantization processing, and quantifies the driving direction of the vehicle according to the position information. The quantization process is as follows:

步骤2.1：将位置信息转换成以视角存在的数字信息，其中，车辆的实际承载角

Figure BDA0001770498450000021
相对于路边单元的定义为：Step 2.1: Convert the position information into digital information that exists in perspective, where the actual bearing angle of the vehicle is
Figure BDA0001770498450000021
The definition relative to the roadside unit is:

Figure BDA0001770498450000022
Figure BDA0001770498450000022

Figure BDA0001770498450000031
Figure BDA0001770498450000031

在此，各参数定义如下：Here, the parameters are defined as follows:

Figure BDA0001770498450000032
在t时刻路边单元与车载系统之间的方位角；
Figure BDA0001770498450000032
The azimuth angle between the roadside unit and the onboard system at time t;

θt：将t时刻的车辆位置转化为方位角的反三角函数；θ t : the inverse trigonometric function that converts the vehicle position at time t into the azimuth angle;

σt：在t时刻由信号反射引起的轴承误差噪声；σ t : bearing error noise caused by signal reflection at time t;

(xt,yt)：在t时刻车辆的当前位置；(x t , y t ): the current position of the vehicle at time t;

步骤2.2：以十字路口中心作为坐标原点，对方向角

Figure BDA0001770498450000033
进行量化处理，确定车辆所在的方向，其中，量化公式如下：Step 2.2: Taking the center of the intersection as the origin of coordinates, the direction angle
Figure BDA0001770498450000033
Perform quantization processing to determine the direction of the vehicle, where the quantization formula is as follows:

Figure BDA0001770498450000034
Figure BDA0001770498450000034

在此，各参数定义如下：Here, the parameters are defined as follows:

bt：t时刻车辆在十字路口的量化方向；b t : the quantized direction of the vehicle at the intersection at time t;

q(·)：量化函数；q( ): quantization function;

i：十字路口的方向标识；i: the direction sign of the intersection;

步骤2.3：为了实现方向信息的具体化，对该方向的车道进行量化，将实际轴承角度

Figure BDA0001770498450000035
重命名为
Figure BDA0001770498450000036
将其记录为：Step 2.3: In order to concretize the direction information, quantify the lane in this direction, and quantify the actual bearing angle
Figure BDA0001770498450000035
renamed to
Figure BDA0001770498450000036
Document it as:

Figure BDA0001770498450000037
Figure BDA0001770498450000037

Figure BDA0001770498450000038
Figure BDA0001770498450000038

在此，各参数定义如下：Here, the parameters are defined as follows:

Figure BDA0001770498450000041
在t时刻路边单元与车载系统之间的方位角；
Figure BDA0001770498450000041
The azimuth angle between the roadside unit and the onboard system at time t;

Figure BDA0001770498450000042
t时刻，将车辆与路边单元的相对位置转化为方位角的反三角函数；
Figure BDA0001770498450000042
At time t, the relative position of the vehicle and the roadside unit is converted into the inverse trigonometric function of the azimuth angle;

(xi,yi)：方向i上路边单元的固定位置；(x i , y i ): the fixed position of the roadside unit in the direction i;

步骤2.4：针对上述量化公式，对车辆所在的方向进行第二次量化，确定车辆所在的车道；Step 2.4: According to the above quantification formula, quantify the direction of the vehicle for the second time, and determine the lane where the vehicle is located;

Figure BDA0001770498450000043
Figure BDA0001770498450000043

在此，各参数定义如下：Here, the parameters are defined as follows:

qt：在t时刻车道的量化方向；q t : the quantized direction of the lane at time t;

ji：位于方向i上的第j个车道；j i : the j-th lane in direction i;

n：车道总数；n: total number of lanes;

3)假设仅使用最近的p+1个车辆速度进行加速度估计，第p个加速度计算方式为：3) Assuming that only the nearest p+1 vehicle speeds are used for acceleration estimation, the p-th acceleration is calculated as:

Figure BDA0001770498450000044
Figure BDA0001770498450000044

在此，各参数定义如下：Here, the parameters are defined as follows:

Δτ：采样时间间隔；Δτ: sampling time interval;

Δv：后一时刻与前一时刻的速度之差；Δv: the speed difference between the next moment and the previous moment;

vt-p：在t-p时刻小车的速度；v tp : the speed of the car at time tp;

τt-p：在t-p时刻小车的时间戳；τ tp : the timestamp of the car at time tp;

at-p：第p个加速度值；a tp : the p-th acceleration value;

此后，根据p个加速度值，利用自回归滑动平均法进行车辆加速度预测，其中，预测公式如下：After that, according to the p acceleration values, the autoregressive moving average method is used to predict the vehicle acceleration, where the prediction formula is as follows:

Figure BDA0001770498450000045
Figure BDA0001770498450000045

在此，各参数定义如下：Here, the parameters are defined as follows:

at：在t时刻小车的加速度；a t : the acceleration of the car at time t;

p：自回归阶数，即加速度总数；p: autoregressive order, that is, the total number of accelerations;

q：移动平均阶数，即滑动总数；q: moving average order, that is, the total number of sliding;

β：不为零的待定系数；β: Undetermined coefficient not zero;

Figure BDA0001770498450000051
不为零的待定系数；
Figure BDA0001770498450000051
a non-zero undetermined coefficient;

ξt：在t时刻独立的误差项；ξ t : independent error term at time t;

4)边缘云服务器针对收集到的车辆信息以及加速度预测值，利用自适应卡尔曼滤波算法对行驶车辆进行速度预测，其中，速度的计算公式为：4) The edge cloud server uses the adaptive Kalman filter algorithm to predict the speed of the moving vehicle according to the collected vehicle information and acceleration prediction value, wherein the calculation formula of the speed is:

vt+1＝vt+at×Δτ；v t +1 = v t +at ×Δτ;

利用状态空间模型将上述公式转化为状态方程和观测方程，其中，方程如下所示：Using the state space model, the above formulas are transformed into state equations and observation equations, where the equations are as follows:

vt+1＝Atvt+Btut+ωt；v t+1 =A t v t +B t u t +ω t ;

zt＝Ctvt+εt；z t =C t v t +ε t ;

在此，各参数定义如下：Here, the parameters are defined as follows:

vt：在t时刻小车的速度；v t : the speed of the car at time t;

At,Bt,Ct：在t时刻的状态转移矩阵；A t , B t , C t : state transition matrix at time t;

ut：在t时刻小车的加速度；u t : the acceleration of the car at time t;

ωt：在t时刻的系统误差，服从高斯分布N(0,Qt)，其中，Qt为在t时刻的过程噪声方差；ω t : systematic error at time t, obeying Gaussian distribution N(0, Q t ), where Q t is the process noise variance at time t;

εt：在t时刻的测量误差，服从高斯分布N(0,Rt)，其中，Rt为在t时刻的测量噪声方差；ε t : measurement error at time t, obeying Gaussian distribution N(0, R t ), where R t is the measurement noise variance at time t;

zt：在t时刻系统的状态观测值；z t : the state observation value of the system at time t;

此后，根据状态空间模型利用自适应卡尔曼滤波算法对车辆速度进行预测，其中，车辆速度更新的步骤如下：After that, the vehicle speed is predicted using the adaptive Kalman filtering algorithm according to the state space model, wherein the steps of updating the vehicle speed are as follows:

步骤4.1：给定初始值

Figure BDA0001770498450000061
和
Figure BDA0001770498450000062
其中，
Figure BDA0001770498450000063
表示t时刻小车水平位置的预测值，
Figure BDA0001770498450000064
表示t时刻小车的误差协方差；Step 4.1: Given initial values
Figure BDA0001770498450000061
and
Figure BDA0001770498450000062
in,
Figure BDA0001770498450000063
represents the predicted value of the horizontal position of the car at time t,
Figure BDA0001770498450000064
represents the error covariance of the car at time t;

步骤4.2：根据给定的初始值

Figure BDA0001770498450000065
计算t时刻的卡尔曼增益值Kt，其中，
Figure BDA0001770498450000066
Step 4.2: According to the given initial value
Figure BDA0001770498450000065
Calculate the K t value of the Kalman gain at time t, where,
Figure BDA0001770498450000066

步骤4.3：根据t时刻的预测值

Figure BDA0001770498450000067
和观测值zt，可以得到当前状态的修正值
Figure BDA0001770498450000068
其中，公式如下：Step 4.3: According to the predicted value at time t
Figure BDA0001770498450000067
and the observed value z t , the correction value of the current state can be obtained
Figure BDA0001770498450000068
Among them, the formula is as follows:

zt＝Ctvt+Rt z t =C t v t +R t

Figure BDA0001770498450000069
Figure BDA0001770498450000069

步骤4.4：更新误差协方差

Figure BDA00017704984500000610
的值，为预测t+1时刻的误差协方差做准备，其中，
Figure BDA00017704984500000611
I为单位阵；Step 4.4: Update the error covariance
Figure BDA00017704984500000610
The value of , in preparation for predicting the error covariance at time t+1, where,
Figure BDA00017704984500000611
I is the unit matrix;

步骤4.5：根据t时刻的修正值

Figure BDA00017704984500000612
以及加速度ut去预测t+1时刻小车的速度
Figure BDA00017704984500000613
其中，
Figure BDA00017704984500000614
Step 4.5: According to the correction value at time t
Figure BDA00017704984500000612
and the acceleration u t to predict the speed of the car at time t+1
Figure BDA00017704984500000613
in,
Figure BDA00017704984500000614

步骤4.6：预测误差协方差，由前一时刻的误差协方差

Figure BDA00017704984500000615
去预测后一时刻的误差协方差
Figure BDA00017704984500000616
其中，
Figure BDA00017704984500000617
μt+1为自适应遗忘因子；Step 4.6: Prediction error covariance, given by the error covariance of the previous moment
Figure BDA00017704984500000615
to predict the error covariance at a later time
Figure BDA00017704984500000616
in,
Figure BDA00017704984500000617
μ t+1 is the adaptive forgetting factor;

步骤4.7：计算t+1时刻的加速度ut+1，其中，计算方法如权利要求1所述3)中的自回归滑动平均法；Step 4.7: calculate the acceleration u t+1 at time t+1 , wherein the calculation method is the autoregressive moving average method in claim 1 3);

步骤4.8：更新迭代次数k为k＝k+1并重新回到步骤4.2开始新一轮的计算；Step 4.8: Update the number of iterations k to k=k+1 and return to step 4.2 to start a new round of calculation;

5)最后，边缘云服务器将处理好的信息通过光缆传送给路边单元，以便于下一次与车载系统的信息交互。5) Finally, the edge cloud server transmits the processed information to the roadside unit through the optical cable, so as to facilitate the next information interaction with the in-vehicle system.

进一步，所述步骤4.6中，自适应遗忘因子的计算公式为：Further, in the step 4.6, the calculation formula of the adaptive forgetting factor is:

μt+1＝max{1,tr(Nt+1)/tr(Mt+1)}；μ t+1 =max{1,tr(N t+1 )/tr(M t+1 )};

Figure BDA0001770498450000071
Figure BDA0001770498450000071

Figure BDA0001770498450000072
Figure BDA0001770498450000072

Figure BDA0001770498450000073
Figure BDA0001770498450000073

Figure BDA0001770498450000074
Figure BDA0001770498450000074

在此，各参数定义如下：Here, the parameters are defined as follows:

Nt+1：在t+1时刻的误差方差，确保其值对称且正定；N t+1 : the error variance at time t+1, make sure its value is symmetric and positive definite;

Lt+1：新息在t+1时刻下的协方差矩阵；L t+1 : the covariance matrix of the innovation at time t+1;

et+1：在t+1时刻测量值与预测值之差，即新息；e t+1 : the difference between the measured value and the predicted value at time t+1, that is, the innovation;

Figure BDA0001770498450000075
在t+1时刻状态转移矩阵C的转置；
Figure BDA0001770498450000075
Transpose of the state transition matrix C at time t+1;

Mt+1：在t+1时刻的误差方差，确保误差协方差

Figure BDA0001770498450000076
的值对称且正定；max{·}：比较后取最大值。M t+1 : Error variance at time t+1, ensuring error covariance
Figure BDA0001770498450000076
The value of is symmetric and positive definite; max{·}: Take the maximum value after comparison.

更进一步，所述步骤1)中，在智能网联交通系统中，路边单元安装在十字路口的红绿灯上且旁边附带有云服务器和定向天线，其中，定线天线的发射角度为60°，使路边单元能更好的与车辆中的车载系统进行信息交互。Further, in the step 1), in the intelligent network-connected transportation system, the roadside unit is installed on the traffic lights at the intersection and is attached with a cloud server and a directional antenna, wherein the emission angle of the directional antenna is 60°, The roadside unit can better interact with the on-board system in the vehicle.

再进一步，所述步骤1.2中，考虑到边缘云服务器的存储容量有限，所以将服务器中的数据每隔一周进行清零。Further, in the step 1.2, considering that the storage capacity of the edge cloud server is limited, the data in the server is cleared every other week.

本发明的技术构思为：首先,在智能网联交通系统中，通过DSRC技术自动识别行驶的车辆并获取相关数据，实现车载系统与路边单元的信息交互。接着,针对采集的相关信息，利用量化公式对路边单元与车载系统的方位角进行量化；利用自回归滑动平均法对加速度进行预测；利用自适应卡尔曼滤波进行速度预测，达到速度修正值。最后，将处理好的信息通过光缆发送给另外3个路边单元，以便于下一次与车载系统的信息交互。The technical idea of the present invention is as follows: First, in the intelligent networked transportation system, the DSRC technology is used to automatically identify the driving vehicle and obtain relevant data, so as to realize the information exchange between the vehicle-mounted system and the roadside unit. Then, according to the collected relevant information, the azimuth angle between the roadside unit and the vehicle system is quantified by the quantization formula; the acceleration is predicted by the autoregressive moving average method; the speed is predicted by the adaptive Kalman filter to achieve the speed correction value. Finally, the processed information is sent to the other 3 roadside units through the optical cable for the next information interaction with the on-board system.

本发明的有益效果主要表现在:1、通过对路边单元与车载系统的方位差的量化，可以清楚的了解当前车辆位于哪个方向的几车道。2、结合自回归滑动平均法和自适应卡尔曼滤波法实现加速度以及速度的预测，并将结果传送给驾驶员，以便驾驶员可以根据车辆的相关信息以及自身经验来做出合适的判断和行为，有效降低了交通事故的发生率。The beneficial effects of the present invention are mainly manifested in: 1. By quantifying the azimuth difference between the roadside unit and the vehicle-mounted system, it is possible to clearly understand which direction and several lanes the current vehicle is located in. 2. Combine the autoregressive moving average method and the adaptive Kalman filtering method to realize the prediction of acceleration and speed, and transmit the results to the driver, so that the driver can make appropriate judgments and behaviors according to the relevant information of the vehicle and their own experience , effectively reducing the incidence of traffic accidents.

附图说明Description of drawings

图1是移动互联交通系统信息交互的示意图。FIG. 1 is a schematic diagram of information interaction in a mobile internet traffic system.

具体实施方式Detailed ways

下面结合附图对本发明作进一步详细描述。The present invention will be described in further detail below with reference to the accompanying drawings.

参照图1，一种基于量化自适应卡尔曼滤波的车辆速度预测方法，本发明基于一种在DSRC技术通讯下的信息交互模型(如图1所示)。在智能网联交通系统中，首先通过量化公式对路边单元与车载系统的方位角进行量化，其次通过自回归滑动平均法对加速度进行预测，最后利用自适应卡尔曼滤波进行速度预测，达到速度修正值，所述预测方法包括以下步骤：Referring to FIG. 1 , a vehicle speed prediction method based on quantization adaptive Kalman filtering, the present invention is based on an information interaction model under DSRC technology communication (as shown in FIG. 1 ). In the intelligent networked transportation system, the azimuth angle between the roadside unit and the vehicle system is firstly quantified by the quantization formula, then the acceleration is predicted by the autoregressive moving average method, and finally the speed is predicted by the adaptive Kalman filter to achieve the speed correction value, the prediction method includes the following steps:

1)在智能网联交通系统中，通过DSRC技术自动识别行驶的车辆并获取相关数据，实现车载系统与路边单元的信息交互，其中，信息交互的步骤如下：1) In the intelligent network-connected transportation system, DSRC technology is used to automatically identify driving vehicles and obtain relevant data, so as to realize the information exchange between the vehicle-mounted system and the roadside unit. The steps of information exchange are as follows:

步骤1.1：当行驶车辆进入定向天线所覆盖的范围内时，车载系统会与路边单元通过DSRC技术实现双向通信，使得双方能同时发送自身存储单元上的信息，其中，车载系统发送的信息包括车辆的当前速度、当前位置和时间戳，路边单元发送的信息包括其他车道上车辆的预测位置、位于哪个方向上、几车道和加速度；Step 1.1: When the driving vehicle enters the range covered by the directional antenna, the on-board system will realize two-way communication with the roadside unit through DSRC technology, so that both parties can send the information on their own storage units at the same time. The information sent by the on-board system includes: The current speed, current position and timestamp of the vehicle, the information sent by the roadside unit includes the predicted position of the vehicle in other lanes, in which direction, how many lanes and acceleration;

步骤1.2：路边单元将获取到的车辆信息发送到边缘云服务器进行一系列的运算操作；Step 1.2: The roadside unit sends the obtained vehicle information to the edge cloud server for a series of computing operations;

2)边缘云服务器根据路边单元与车载系统之间的方位差，进行方位角计算并做相应的量化处理，针对位置信息对车辆行驶方向进行量化，其中，量化过程为：2) According to the azimuth difference between the roadside unit and the vehicle-mounted system, the edge cloud server calculates the azimuth angle and performs corresponding quantization processing, and quantifies the driving direction of the vehicle according to the position information. The quantization process is as follows:

步骤2.1：将位置信息转换成以视角存在的数字信息，其中，车辆的实际承载角

Figure BDA0001770498450000091
相对于路边单元的定义为：Step 2.1: Convert the position information into digital information that exists in perspective, where the actual bearing angle of the vehicle is
Figure BDA0001770498450000091
The definition relative to the roadside unit is:

Figure BDA0001770498450000092
Figure BDA0001770498450000092

Figure BDA0001770498450000093
Figure BDA0001770498450000093

在此，各参数定义如下：Here, the parameters are defined as follows:

Figure BDA0001770498450000094
在t时刻路边单元与车载系统之间的方位角；
Figure BDA0001770498450000094
The azimuth angle between the roadside unit and the onboard system at time t;

θt：将t时刻的车辆位置转化为方位角的反三角函数；θ t : the inverse trigonometric function that converts the vehicle position at time t into the azimuth angle;

σt：在t时刻由信号反射引起的轴承误差噪声；σ t : bearing error noise caused by signal reflection at time t;

(xt,yt)：在t时刻车辆的当前位置；(x t , y t ): the current position of the vehicle at time t;

步骤2.2：以十字路口中心作为坐标原点，对方向角

Figure BDA0001770498450000101
进行量化处理，确定车辆所在的方向，其中，量化公式如下：Step 2.2: Taking the center of the intersection as the origin of coordinates, the direction angle
Figure BDA0001770498450000101
Perform quantization processing to determine the direction of the vehicle, where the quantization formula is as follows:

Figure BDA0001770498450000102
Figure BDA0001770498450000102

在此，各参数定义如下：Here, the parameters are defined as follows:

bt：t时刻车辆在十字路口的量化方向；b t : the quantized direction of the vehicle at the intersection at time t;

q(·)：量化函数；q( ): quantization function;

i：十字路口的方向标识；i: the direction sign of the intersection;

步骤2.3：为了实现方向信息的具体化，对该方向的车道进行量化，将实际轴承角度

Figure BDA0001770498450000103
重命名为
Figure BDA0001770498450000104
将其记录为：Step 2.3: In order to concretize the direction information, quantify the lane in this direction, and quantify the actual bearing angle
Figure BDA0001770498450000103
renamed to
Figure BDA0001770498450000104
Document it as:

Figure BDA0001770498450000105
Figure BDA0001770498450000105

Figure BDA0001770498450000106
Figure BDA0001770498450000106

在此，各参数定义如下：Here, the parameters are defined as follows:

Figure BDA0001770498450000107
在t时刻路边单元与车载系统之间的方位角；
Figure BDA0001770498450000107
The azimuth angle between the roadside unit and the onboard system at time t;

Figure BDA0001770498450000108
t时刻，将车辆与路边单元的相对位置转化为方位角的反三角函数；(xi,yi)：方向i上路边单元的固定位置；
Figure BDA0001770498450000108
At time t, the relative position of the vehicle and the roadside unit is converted into the inverse trigonometric function of the azimuth angle; (x i , y i ): the fixed position of the roadside unit in the direction i;

步骤2.4：针对上述量化公式，对车辆所在的方向进行第二次量化，确定车辆所在的车道；Step 2.4: According to the above quantification formula, quantify the direction of the vehicle for the second time, and determine the lane where the vehicle is located;

Figure BDA0001770498450000109
Figure BDA0001770498450000109

在此，各参数定义如下：Here, the parameters are defined as follows:

qt：在t时刻车道的量化方向；q t : the quantized direction of the lane at time t;

ji：位于方向i上的第j个车道；j i : the j-th lane in direction i;

n：车道总数；n: total number of lanes;

3)假设仅使用最近的p+1个车辆速度进行加速度估计，第p个加速度计算方式为：3) Assuming that only the nearest p+1 vehicle speeds are used for acceleration estimation, the p-th acceleration is calculated as:

Figure BDA0001770498450000111
Figure BDA0001770498450000111

在此，各参数定义如下：Here, the parameters are defined as follows:

Δτ：采样时间间隔；Δτ: sampling time interval;

Δv：后一时刻与前一时刻的速度之差；Δv: the speed difference between the next moment and the previous moment;

vt-p：在t-p时刻小车的速度；v tp : the speed of the car at time tp;

τt-p：在t-p时刻小车的时间戳；τ tp : the timestamp of the car at time tp;

at-p：第p个加速度值；a tp : the p-th acceleration value;

此后，根据p个加速度值，利用自回归滑动平均法进行车辆加速度预测，其中，预测公式如下：After that, according to the p acceleration values, the autoregressive moving average method is used to predict the vehicle acceleration, where the prediction formula is as follows:

Figure BDA0001770498450000112
Figure BDA0001770498450000112

在此，各参数定义如下：Here, the parameters are defined as follows:

at：在t时刻小车的加速度；a t : the acceleration of the car at time t;

p：自回归阶数，即加速度总数；p: autoregressive order, that is, the total number of accelerations;

q：移动平均阶数，即滑动总数；q: moving average order, that is, the total number of sliding;

β：不为零的待定系数；β: Undetermined coefficient not zero;

Figure BDA0001770498450000113
不为零的待定系数；
Figure BDA0001770498450000113
a non-zero undetermined coefficient;

ξt：在t时刻独立的误差项；ξ t : independent error term at time t;

4)边缘云服务器针对收集到的车辆信息以及加速度预测值，利用自适应卡尔曼滤波算法对行驶车辆进行速度预测，其中，速度的计算公式为：4) The edge cloud server uses the adaptive Kalman filter algorithm to predict the speed of the moving vehicle according to the collected vehicle information and acceleration prediction value, wherein the calculation formula of the speed is:

vt+1＝vt+at×Δτ；v t +1 = v t +at ×Δτ;

利用状态空间模型将上述公式转化为状态方程和观测方程，其中，方程如下所示：Using the state space model, the above formulas are transformed into state equations and observation equations, where the equations are as follows:

vt+1＝Atvt+Btut+ωt；v t+1 =A t v t +B t u t +ω t ;

zt＝Ctvt+εt；z t =C t v t +ε t ;

在此，各参数定义如下：Here, the parameters are defined as follows:

vt：在t时刻小车的速度；v t : the speed of the car at time t;

At,Bt,Ct：在t时刻的状态转移矩阵；A t , B t , C t : state transition matrix at time t;

ut：在t时刻小车的加速度；u t : the acceleration of the car at time t;

ωt：在t时刻的系统误差，服从高斯分布N(0,Qt)，其中，Qt为在t时刻的过程噪声方差；ω t : systematic error at time t, obeying Gaussian distribution N(0, Q t ), where Q t is the process noise variance at time t;

εt：在t时刻的测量误差，服从高斯分布N(0,Rt)，其中，Rt为在t时刻的测量噪声方差；ε t : measurement error at time t, obeying Gaussian distribution N(0, R t ), where R t is the measurement noise variance at time t;

zt：在t时刻系统的状态观测值；z t : the state observation value of the system at time t;

此后，根据状态空间模型利用自适应卡尔曼滤波算法对车辆速度进行预测，其中，车辆速度更新的步骤如下：After that, the vehicle speed is predicted using the adaptive Kalman filtering algorithm according to the state space model, wherein the steps of updating the vehicle speed are as follows:

步骤4.1：给定初始值

Figure BDA0001770498450000121
和
Figure BDA0001770498450000122
其中，
Figure BDA0001770498450000123
表示t时刻小车水平位置的预测值，
Figure BDA0001770498450000124
表示t时刻小车的误差协方差；Step 4.1: Given initial values
Figure BDA0001770498450000121
and
Figure BDA0001770498450000122
in,
Figure BDA0001770498450000123
represents the predicted value of the horizontal position of the car at time t,
Figure BDA0001770498450000124
represents the error covariance of the car at time t;

步骤4.2：根据给定的初始值

Figure BDA0001770498450000125
计算t时刻的卡尔曼增益值Kt，其中，
Figure BDA0001770498450000126
Step 4.2: According to the given initial value
Figure BDA0001770498450000125
Calculate the K t value of the Kalman gain at time t, where,
Figure BDA0001770498450000126

步骤4.3：根据t时刻的预测值

Figure BDA0001770498450000127
和观测值zt，可以得到当前状态的修正值
Figure BDA0001770498450000128
其中，公式如下：Step 4.3: According to the predicted value at time t
Figure BDA0001770498450000127
and the observed value z t , the correction value of the current state can be obtained
Figure BDA0001770498450000128
Among them, the formula is as follows:

zt＝Ctvt+Rt z t =C t v t +R t

Figure BDA0001770498450000131
Figure BDA0001770498450000131

步骤4.4：更新误差协方差

Figure BDA0001770498450000132
的值，为预测t+1时刻的误差协方差做准备，其中，
Figure BDA0001770498450000133
I为单位阵；Step 4.4: Update the error covariance
Figure BDA0001770498450000132
The value of , in preparation for predicting the error covariance at time t+1, where,
Figure BDA0001770498450000133
I is the unit matrix;

步骤4.5：根据t时刻的修正值

Figure BDA0001770498450000134
以及加速度ut去预测t+1时刻小车的速度
Figure BDA0001770498450000135
其中，
Figure BDA0001770498450000136
Step 4.5: According to the correction value at time t
Figure BDA0001770498450000134
and the acceleration u t to predict the speed of the car at time t+1
Figure BDA0001770498450000135
in,
Figure BDA0001770498450000136

步骤4.6：预测误差协方差，由前一时刻的误差协方差

Figure BDA0001770498450000137
去预测后一时刻的误差协方差
Figure BDA0001770498450000138
其中，
Figure BDA0001770498450000139
μt+1为自适应遗忘因子；Step 4.6: Prediction error covariance, given by the error covariance of the previous moment
Figure BDA0001770498450000137
to predict the error covariance at a later time
Figure BDA0001770498450000138
in,
Figure BDA0001770498450000139
μ t+1 is the adaptive forgetting factor;

步骤4.7：计算t+1时刻的加速度ut+1，其中，计算方法如权利要求1所述3)中的自回归滑动平均法；Step 4.7: calculate the acceleration u t+1 at time t+1 , wherein the calculation method is the autoregressive moving average method in claim 1 3);

步骤4.8：更新迭代次数k为k＝k+1并重新回到步骤4.2开始新一轮的计算；Step 4.8: Update the number of iterations k to k=k+1 and return to step 4.2 to start a new round of calculation;

5)最后，边缘云服务器将处理好的信息通过光缆传送给路边单元，以便于下一次与车载系统的信息交互。5) Finally, the edge cloud server transmits the processed information to the roadside unit through the optical cable, so as to facilitate the next information interaction with the in-vehicle system.

进一步，所述步骤4.6中，自适应遗忘因子的计算公式为：Further, in the step 4.6, the calculation formula of the adaptive forgetting factor is:

μt+1＝max{1,tr(Nt+1)/tr(Mt+1)}；μ t+1 =max{1,tr(N t+1 )/tr(M t+1 )};

Figure BDA00017704984500001310
Figure BDA00017704984500001310

Figure BDA00017704984500001311
Figure BDA00017704984500001311

Figure BDA00017704984500001312
Figure BDA00017704984500001312

Figure BDA00017704984500001313
Figure BDA00017704984500001313

在此，各参数定义如下：Here, the parameters are defined as follows:

Nt+1：在t+1时刻的误差方差，确保其值对称且正定；N t+1 : the error variance at time t+1, make sure its value is symmetric and positive definite;

Lt+1：新息在t+1时刻下的协方差矩阵；L t+1 : the covariance matrix of the innovation at time t+1;

et+1：在t+1时刻测量值与预测值之差，即新息；e t+1 : the difference between the measured value and the predicted value at time t+1, that is, the innovation;

Figure BDA0001770498450000141
在t+1时刻状态转移矩阵C的转置；
Figure BDA0001770498450000141
Transpose of the state transition matrix C at time t+1;

Mt+1：在t+1时刻的误差方差，确保误差协方差

Figure BDA0001770498450000142
的值对称且正定；max{·}：比较后取最大值。M t+1 : Error variance at time t+1, ensuring error covariance
Figure BDA0001770498450000142
The value of is symmetric and positive definite; max{·}: Take the maximum value after comparison.

更进一步，所述步骤1)中，在智能网联交通系统中，路边单元安装在十字路口的红绿灯上且旁边附带有云服务器和定向天线，其中，定线天线的发射角度为60°，使路边单元能更好的与车辆中的车载系统进行信息交互。Further, in the step 1), in the intelligent network-connected transportation system, the roadside unit is installed on the traffic lights at the intersection and is attached with a cloud server and a directional antenna, wherein the emission angle of the directional antenna is 60°, The roadside unit can better interact with the on-board system in the vehicle.

再进一步，所述步骤1.2中，考虑到边缘云服务器的存储容量有限，所以将服务器中的数据每隔一周进行清零。Further, in the step 1.2, considering that the storage capacity of the edge cloud server is limited, the data in the server is cleared every other week.

Claims (4)

1. A vehicle speed prediction method based on quantization adaptive Kalman filtering is characterized by comprising the following steps:
1) in an intelligent network traffic system, a vehicle is automatically identified through a DSRC technology, relevant data are obtained, and information interaction between a vehicle-mounted system and a roadside unit is realized, wherein the information interaction comprises the following steps:
step 1.1: when a running vehicle enters the range covered by the directional antenna, the vehicle-mounted system and the roadside unit realize two-way communication through the DSRC technology, so that the two sides can simultaneously transmit information on the storage units of the vehicle-mounted system, wherein the information transmitted by the vehicle-mounted system comprises the current speed, the current position and the time stamp of the vehicle, and the information transmitted by the roadside unit comprises the predicted position of the vehicle on other lanes, the direction in which the vehicle is located, several lanes and the acceleration;
step 1.2: the roadside unit sends the acquired vehicle information to an edge cloud server to perform a series of operation operations;
2) the edge cloud server calculates an azimuth angle and performs corresponding quantization processing according to the azimuth difference between the roadside unit and the vehicle-mounted system, and quantizes the vehicle driving direction according to the position information, wherein the quantization process comprises the following steps:
step 2.1: converting the position information into digital information existing at a viewing angle, wherein the azimuth angle between the roadside unit and the on-board system of the vehicle at the time t
Figure FDA0002484485270000011
The definition with respect to a roadside unit is:
Figure 1
Figure FDA0002484485270000021
here, the parameters are defined as follows:
Figure FDA0002484485270000022
azimuth angle between roadside unit and vehicle system at time t;
θt: converting the vehicle position at the time t into an inverse trigonometric function of the azimuth angle;
σt: bearing error noise caused by signal reflection at time t;
(xt,yt): the current position of the vehicle at time t;
step 2.2: using the center of the crossroad as the origin of coordinates, aiming at the azimuth angle between the roadside unit and the vehicle-mounted system at the moment t
Figure FDA0002484485270000023
And performing quantization processing to determine the direction of the vehicle, wherein the quantization formula is as follows:
Figure FDA0002484485270000024
here, the parameters are defined as follows:
bt: the quantitative direction of the vehicle at the crossroad at the moment t;
q (·): a quantization function;
i: direction identification of the crossroad;
step 2.3: in order to realize the concretization of the direction information, the lane of the direction is quantified, and the azimuth angle between the roadside unit and the vehicle-mounted system at the time t
Figure FDA0002484485270000025
Rename it to
Figure FDA0002484485270000026
It is recorded as:
Figure FDA0002484485270000031
Figure FDA0002484485270000032
here, the parameters are defined as follows:
θt': at the moment t, converting the relative position of the vehicle and the roadside unit into an inverse trigonometric function of the azimuth angle;
(xi,yi): the fixed position of the roadside unit in the direction i;
step 2.4: according to the quantization formula, performing second quantization on the direction of the vehicle to determine the lane of the vehicle;
Figure FDA0002484485270000033
here, the parameters are defined as follows:
qt: the quantized direction of the lane at time t;
ji: the jth lane in direction i;
n: total number of lanes;
3) assuming that only the latest p +1 vehicle speeds are used for acceleration estimation, the pth acceleration calculation method is:
Figure FDA0002484485270000034
here, the parameters are defined as follows:
Δ τ: a sampling time interval;
Δ v: the difference in velocity between the next time and the previous time;
vt-p: the speed of the trolley at the time t-p;
τt-p: a timestamp of the trolley at the time t-p;
at-p: the p-th acceleration value;
thereafter, vehicle acceleration prediction is performed by using an autoregressive moving average method according to the p acceleration values, wherein the prediction formula is as follows:
Figure FDA0002484485270000041
here, the parameters are defined as follows:
at: acceleration of the trolley at time t;
p: the autoregressive order, namely the total acceleration;
q: moving average order, i.e., total number of slips;
β, undetermined coefficient not equal to zero;
Figure FDA0002484485270000042
undetermined coefficients other than zero;
ξt: an error term independent at time t;
4) the method comprises the following steps that the edge cloud server predicts the speed of a running vehicle by using an adaptive Kalman filtering algorithm according to collected vehicle information and an acceleration predicted value, wherein the calculation formula of the speed is as follows:
vt+1＝vt+at×Δτ；
converting the above formula into a state equation and an observation equation by using a state space model, wherein the equations are as follows:
vt+1＝Atvt+Btut+ωt；
zt＝Ctvt+t；
here, the parameters are defined as follows:
vt: the speed of the trolley at time t;
At,Bt,Ct: a state transition matrix at time t;
ut: acceleration of the trolley at time t;
ωt: the systematic error at time t obeys the Gaussian distribution N (0, Q)t) Wherein Q istIs the process noise variance at time t;
t: measurement error at time t obeys Gaussian distribution N (0, R)t) Wherein R istIs the measured noise variance at time t;
zt: a state observation of the system at time t;
and then, predicting the vehicle speed by using an adaptive Kalman filtering algorithm according to the state space model, wherein the vehicle speed is updated by the following steps:
step 4.1: giving an initial value
Figure FDA0002484485270000051
And
Figure FDA0002484485270000052
wherein,
Figure FDA0002484485270000053
showing the predicted value of the horizontal position of the trolley at the moment t,
Figure FDA0002484485270000054
representing the error covariance of the trolley at the time t;
step 4.2: according to a given initial value
Figure FDA0002484485270000055
Calculating Kalman gain value K at t momenttWherein
Figure FDA0002484485270000056
step 4.3: predicted value according to time t
Figure FDA0002484485270000057
And the observed value ztThe corrected value of the current state can be obtained
Figure FDA0002484485270000058
Wherein, the formula is as follows:
zt＝Ctvt+Rt
Figure FDA0002484485270000059
step 4.4: updating error covariance
Figure FDA00024844852700000510
In preparation for predicting the error covariance at time t +1, wherein,
Figure FDA00024844852700000511
i is a unit array;
step 4.5: correction value according to time t
Figure FDA00024844852700000512
And acceleration utTo predict the speed of the car at the time t +1
Figure FDA00024844852700000513
Wherein,
Figure FDA00024844852700000514
step 4.6: prediction of error covariance from the error covariance of the previous time instant
Figure FDA00024844852700000515
To predict the error covariance of the next time instant
Figure FDA00024844852700000516
Wherein,
Figure FDA00024844852700000517
μt+1is an adaptive forgetting factor;
step 4.7: calculating the acceleration u at time t +1t+1Wherein, the calculation method is the autoregressive moving average method in the 3);
step 4.8: updating the iteration number k to k +1, and returning to the step 4.2 to start a new round of calculation;
5) and finally, the edge cloud server transmits the processed information to the roadside unit through an optical cable so as to facilitate the information interaction with the vehicle-mounted system next time.
2. The vehicle speed prediction method based on the quantized adaptive kalman filter according to claim 1, wherein: in the step 1), in the intelligent network traffic system, the roadside units are installed on traffic lights of the crossroad and are attached with the cloud server and the directional antenna, wherein the emission angle of the routing antenna is 60 degrees, so that the roadside units can better perform information interaction with a vehicle-mounted system in a vehicle.
3. A vehicle speed prediction method based on quantization adaptive kalman filtering according to claim 1 or 2, characterized in that: in step 4.6, the calculation formula of the adaptive forgetting factor is as follows:
μt+1＝max{1,tr(Nt+1)/tr(Mt+1)}；
Figure FDA0002484485270000061
Figure FDA0002484485270000062
Figure FDA0002484485270000063
Figure FDA0002484485270000064
here, the parameters are defined as follows:
Nt+1: the error variance at the moment of t +1 ensures that the values are symmetrical and positive;
Lt+1: covariance matrix of innovation at time t + 1;
et+1: the difference between the measured value and the predicted value at the moment t +1 is the innovation;
Figure FDA0002484485270000065
transposing the state transition matrix C at time t + 1;
Mt+1: error variance at time t +1, ensuring error covariance
Figure FDA0002484485270000066
The values of (a) are symmetrical and positive;
max {. cndot ]: and taking the maximum value after comparison.
4. A vehicle speed prediction method based on quantization adaptive kalman filtering according to claim 1 or 2, characterized in that: in step 1.2, the data in the server is cleared every other week in consideration of the limited storage capacity of the edge cloud server.
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