CN115081927B - Evaluation and prediction method of road friction coefficient - Google Patents
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Abstract
本发明公开了一种路面摩擦系数评估与预测方法，包括如下步骤：S1、采集车辆与环境信息；S2、分析评估摩擦系数基础数据；S3、测算路面摩擦系数；S4、摩擦系数大数据评估；S5、摩擦系数预警预测。本发明可获得精确至复杂环境下的车道级实时路面摩擦系数评估值，更接近实际情况，并构建了路面摩擦系数车联网系统包括车载终端、大数据云计算处理平台以及路面摩擦系数预警平台，可用于路面车辆实时的路面摩擦系数预警以及提供中长期解决方案。
The present invention discloses a road friction coefficient evaluation and prediction method, comprising the following steps: S1, collecting vehicle and environment information; S2, analyzing and evaluating basic friction coefficient data; S3, measuring road friction coefficient; S4, evaluating friction coefficient big data; S5, predicting friction coefficient warning. The present invention can obtain lane-level real-time road friction coefficient evaluation values accurate to complex environments, which is closer to the actual situation, and constructs a road friction coefficient Internet of Vehicles system including an on-board terminal, a big data cloud computing processing platform, and a road friction coefficient warning platform, which can be used for real-time road friction coefficient warnings for road vehicles and provide medium- and long-term solutions.
Description
Technical Field
The invention belongs to the technical field of urban traffic road engineering safe driving, and particularly relates to a road surface friction coefficient evaluation and prediction method.
Background
Due to factors such as vehicle load and environment, the anti-skid performance between the vehicle and the road surface is weakened, and frequent traffic accidents are often caused. In research and investigation in the countries such as English and Japanese, the traffic accidents caused by the reduction of the road surface anti-skid performance each year account for about 1/5 of the total number. With the rapid development of national economy in China, the high-grade highways and urban roads in China have large traffic flow and high speed, the requirements on the road surface anti-skid capability are improved, and the influence of road construction and maintenance management departments on the road surface anti-skid capability on traffic safety is emphasized.
At present, the testing and evaluation of the anti-skid performance of the pavement in China face a plurality of problems that firstly, the sanding method, the pendulum friction meter method and the DF tester method in the construction depth method adopt manual detection, the detection efficiency is low, and the construction depth is only an indirect index. The laser scanning construction depth method and the transverse force coefficient tester method have high automation degree and high detection efficiency, but the test is extremely easy to be influenced by external environment, the detection result is easy to be influenced by conditions of the road surface environment such as ponding, snow accumulation, mud and the like, and in addition, the test is required to be carried out in closed traffic or low traffic. The testing methods such as a formulated distance method and a locked wheel trailer method are mainly used for friction resistance between the vehicle tires and the road surface after the vehicle brakes and the wheels are locked, the automation level and the efficiency are high, but the testing method can only be carried out in a annual inspection mode, the empty window period is long, the working condition scene is fair, and the real-time performance is not enough. The friction force of the road surface in the braking state of the vehicle is directly related to the driving safety, and the friction between the tire and the road surface is different from the traditional static friction and dynamic friction, is peristaltic friction and is directly related to the vehicle speed. Therefore, the friction coefficient of the test vehicle in the actual braking state has important significance on the road surface anti-skid capability.
The technology of the internet of vehicles and the rapid development of big data enable information of vehicles and roads to be perceived in real time and to be gathered, analyzed and utilized. At present, the detection of the skid resistance of the domestic road surface is still in the traditional manual or automatic stage, the provided skid resistance coefficient data has hysteresis, discontinuity and unilateral property, the actual vehicle and the road surface state have real-time property, and the traditional skid resistance data can not provide reliable skid resistance insufficient early warning for the running of the vehicle in a braking state. Therefore, the road surface friction coefficient assessment and early warning prediction method based on the vehicle braking state and the large data of the Internet of vehicles is provided, a road surface friction coefficient prediction model is built based on large data analysis of massive data, so that the lane-level road surface friction coefficient under a complex environment is estimated in real time, on one hand, a large data analysis platform library and a road surface friction coefficient prediction model are continuously updated through historical data, on the other hand, an instantaneous road surface friction coefficient predicted value and a threshold value are provided according to an actual driving environment, and timely early warning and a solution are provided for the reduction of the road surface friction coefficient, so that traffic safety accidents caused by the reduction of the road surface anti-skid capability are avoided.
Disclosure of Invention
The invention aims to provide an evaluation and prediction method for the road friction coefficient, which can evaluate the road friction coefficient of a vehicle in a complex environment in real time based on a road friction coefficient pre-estimation model constructed by big data analysis and provide early warning information and a solution for insufficient road friction for a driving vehicle.
A road friction coefficient evaluation and prediction method comprises the following steps:
S1, collecting vehicle and environment information;
s2, analyzing and evaluating friction coefficient basic data;
s3, measuring and calculating the friction coefficient of the road surface;
s4, evaluating friction coefficient big data;
s5, friction coefficient early warning prediction.
Further, in S1, the vehicle and the environmental information are acquired through sensing, and the data information of the vehicle in the braking process is monitored by a vehicle monitoring system and various sensors, so as to obtain the related data of the vehicle and the road where the vehicle is located in the braking process.
Further, in S2, the friction coefficient basic data collected in S1 are located and uploaded to the cloud platform, the data collected by sensing vehicles at different positions are uploaded to the big data cloud platform through the internet of vehicles system, and the big data cloud platform is stored according to the types, and further processing, analysis and feedback are performed.
Further, in S3, the road surface friction coefficient formula in the vehicle braking state is as follows:
a=a1+a2
wherein mu is the estimated friction coefficient of the road surface,
Mu 1 -friction coefficient between brake pad and brake disc,
F N, the unit is N,
R is the radius of the brake wheel, the unit is m,
R 1 -radius of brake block, unit is m,
M-the mass of the brake wheel, the unit is kg,
W, vehicle load, in N,
A, tire rotation deceleration, the unit is rad.s -2,
A 1, which is the tire rotation deceleration caused by wind resistance, the unit is rad.s -2,
A 2, the tire rotation deceleration caused by braking, the unit is rad.s -2,
F, wind resistance, wherein the unit is N.
Further, in S4, the large data of the friction coefficient is evaluated, the average value of the friction coefficient in different states is calculated by collecting the estimated values of the friction coefficient of the road surface in different vehicles, speeds, positions, humidity and temperatures, a real-time evaluation model of the friction coefficient of the road surface is built by accumulating mass data, and the threshold value of the friction coefficient of the road surface in different states is designed.
Further, in S5, the friction coefficient early warning prediction gives a real-time vehicle friction coefficient threshold according to the real-time vehicle and the environmental conditions of the trip, and when the estimated value of the road friction coefficient exceeds the threshold in the running process of the vehicle, the big data cloud platform can remotely send a vehicle early warning signal to the vehicle through the wireless transmission module, and provides a corresponding solution (instantaneous deceleration, medium-term tire or brake pad replacement, long-term road surface replacement).
The beneficial effects of the invention are as follows:
(1) According to the invention, the data collected by monitoring the relevant information of vehicle road surface friction is collected to a big data platform by utilizing a wireless transmission module;
(2) The invention provides a pavement friction coefficient evaluation method, which is based on the data volume of a big data platform, establishes a pavement friction coefficient estimation model under a complex state after deep processing, and gives out thresholds under different states;
(3) The invention provides a road friction coefficient early-warning prediction method, which gives out an estimated value and a threshold value of the road friction coefficient in real time, provides early-warning prompts and deceleration suggestions, and provides a medium-long term solution for drivers and road maintenance departments according to long-term accumulated data;
(4) The invention constructs the road surface friction coefficient evaluation and early warning prediction method based on the vehicle braking state and the large data of the vehicle networking, can obtain the road surface friction coefficient evaluation value which is accurate to the lane level and real-time and complex environment, is closer to the actual situation, constructs the vehicle networking system for road surface friction coefficient evaluation and early warning, and can be used for real-time road surface friction coefficient monitoring and early warning of road vehicles and provides a medium-long term solution.
Drawings
FIG. 1 is a flow chart of the work flow of the Internet of vehicles system for road friction coefficient assessment and early warning of the present invention;
FIG. 2 is a schematic layout diagram of a vehicle terminal according to the present invention;
FIG. 3 is a schematic diagram of the calculation parameters of the road friction coefficient according to the present invention;
FIG. 4 is a schematic diagram of a big data cloud computing platform according to the present invention;
FIG. 5 is a schematic diagram of a road friction coefficient early warning platform according to the invention;
FIG. 6 is a plot of lane-level road friction coefficient hot spot profile of the present invention;
In the figures 1-6, 1, a vehicle-mounted terminal, 1.1, vehicle information, 1.2, a vehicle sensor, 1.3, environment information, 1.4, an environment sensor, 1.5, road monitoring equipment, 1.6, road information, 2, a mobile base station, 3, a wireless data communication gateway, 4, the Internet, 5, a mining control gateway, 6, a big data cloud computing processing platform, 7, a road friction coefficient early warning platform, 7.1, a road friction coefficient real-time monitoring module, 7.2, a road friction coefficient early warning prompt module, 7.3, a road friction coefficient solution supply module, 8, lane-level road friction coefficient hot spot distribution conditions, 8.1, a first right lane, 8.2, a second right lane, 8.3, a right lane, 8.4, a central separation belt, 8.5, a left lane, 8.6, a second left lane, 8.7 and a first left lane.
Detailed Description
As shown in fig. 1-6, the internet of vehicles system for road friction coefficient evaluation and early warning prediction scheme in the invention comprises an information-aware vehicle-mounted terminal 1, a mobile base station 2 in a wireless transmission module, a wireless data communication gateway 3, an internet 4, a mining control gateway 5 big data cloud computing processing platform 6 and a road friction coefficient early warning platform 7. The vehicle-mounted terminal 1 collects data acquired by the sensors (1.2, 1.4) and the monitor 1.5, then the wireless transmission modules (2, 3, 4, 5) are utilized to transmit the data to the big data cloud computing processing platform 6 for real-time estimation of the road friction coefficient and system construction under the complex environment, and finally the road friction coefficient early warning platform 7 is utilized to provide early warning prompt and solution for the vehicle-mounted terminal, so that the problem of vehicle traffic safety caused by insufficient road anti-skid capability is avoided.
In the invention, the vehicle-mounted terminal 1 collects vehicle information 1.1, environment information 1.3 and road information 1.6 respectively by using a vehicle sensor 1.2, an environment sensor 1.4 and road monitoring equipment 1.5, then collects the vehicle information, the environment information 1.3 and the road information 1.6 to the vehicle-mounted terminal 1, transmits the vehicle-mounted information to the Internet 4 through a mobile base station 2 and a wireless data communication gateway 3, and then collects the vehicle-mounted information to a big data cloud computing processing platform 6 by using a mining control gateway 5 for storage and analysis.
The vehicle information 1.1 collected by the vehicle sensor 1.2 is specifically vehicle load, brake braking force, tire rotation deceleration caused by braking, brake wheel radius, friction coefficient between a brake pad and a brake disc, brake pad radius and brake wheel mass, wherein the vehicle load, the brake braking force and the tire rotation deceleration caused by braking are variable information, the vehicle sensor 1.2 is required to monitor in real time, and the brake wheel radius, the friction coefficient between the brake pad and the brake disc, the brake pad radius and the brake wheel mass are long-term constants and are obtained through a vehicle system.
The environmental information 1.3 collected by the environmental sensor 1.4 is specifically humidity, temperature and wind resistance, and the road information 1.6 collected by the road monitoring equipment 1.5 is specifically road name, road section position and lane of a running vehicle in real time.
According to the invention, the big data cloud computing processing platform 6 is divided into a data collecting, analyzing and feeding back module, and the data collecting module is used for carrying out reconfiguration on the data collected by the vehicle-mounted terminal 1 and storing the data according to classes, namely, classifying and storing the data according to the types of vehicles, driving positions, vehicle speeds, environments (temperature and humidity) and the like.
According to the invention, the big data cloud computing processing platform 6 data analysis module obtains the road friction coefficient estimation value through the computation of the vehicle information 1.1, the environment information 1.3 and the road information 1.6, determines the threshold value of the road friction coefficient under different states according to the road friction coefficient estimation system constructed by long-term big data statistics, and continuously optimizes and updates the road friction coefficient estimation system according to the data transmitted in real time.
According to the invention, a data feedback module of the big data cloud computing processing platform 6 transmits the data feedback system to a vehicle-mounted terminal road friction coefficient pre-estimation value in real time according to real-time vehicle information 1.1, environment information 1.3, road information 1.6 and a road friction coefficient estimation system, and provides a reference threshold value.
The construction content of the estimated model adopted by the real-time estimation method of the road friction coefficient comprises the following steps:
a. Estimating a training sample database based on road friction coefficients of different driving states, wherein each sample data comprises 5 components including a vehicle type, a vehicle speed, a temperature, a humidity and a vehicle position;
b. Establishing a road friction coefficient estimation BP neural network for each driving state, and carrying out network initialization, wherein the initialization comprises maximum training times, learning accuracy, hidden node number, initial weight, threshold value and initial learning rate;
c. Inputting training samples X1,X2,……,Xk,Xk＝[xk1,xk2,xk3,xk4,xk5],, wherein k represents the number of the training samples, the 5 components respectively correspond to the vehicle type, the vehicle speed, the temperature, the humidity and the vehicle position, and the training samples are uniformly distributed in each Map node;
d. Reading in a network weight record stored in the Hadoop distributed file system by using a Map function, obtaining and outputting the weight of each Map node, reading in the network weight record stored in the Hadoop distributed file system by using a Reduce function, receiving the weight output by the Map, and judging whether the next cycle is needed or not according to the difference value between the updated network weight of the Map node and the Hadoop network weight;
e. If the network weight is not updated any more, the training of the BP neural network algorithm for estimating the road friction coefficient is finished;
f. and estimating the road surface friction coefficient of the vehicle-mounted terminal by using a trained road surface friction coefficient estimation BP neural network algorithm.
The road friction coefficient early warning platform comprises a road friction coefficient real-time monitoring module 7.1, a road friction coefficient early warning prompt module 7.2 and a road friction coefficient solution supply module 7.3.
In the invention, the real-time monitoring module 7.1 of the road friction coefficient displays real-time road friction coefficient evaluation value and threshold value, and real-time parameters such as temperature, humidity, vehicle speed and the like are included, and the vehicle road friction coefficient value obtained by the transmission of the Internet of vehicles is compared with the threshold value provided by big data.
The road friction coefficient early warning prompt module 7.2 provides real-time early warning prompt for the vehicle terminal, namely, sends out warning prompt when the road friction coefficient is insufficient.
The road friction coefficient solution supply module 7.3 of the present invention provides solutions according to early warning cues, including instantaneous deceleration solutions, medium-term tire replacement and brake pad solutions and long-term road surface update solutions, such as the ultra-thin wearing layer additionally paved. The deceleration scheme is a solution provided immediately after the early warning information appears, the scheme of replacing tires and brake pads in the middle period is a scheme adopted when the instantaneous scheme is poor in solution effect, the long-term pavement updating scheme is a scheme adopted when the first two schemes cannot be effectively solved, and the cost of the three schemes is gradually increased.
According to the invention, for the road surface friction coefficients of different states (vehicle type, vehicle speed, temperature and humidity), the excellent medium-difference threshold values of 4 grades are designed, the road surface friction coefficients of the lane grades are displayed in real time, and the distribution situation of the road surface friction coefficients of each lane is real-time in fig. 6.
The above embodiments are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereto, and any modification made on the basis of the technical scheme according to the technical idea of the present invention falls within the protection scope of the present invention.
Claims (4)
1. The road friction coefficient evaluation and prediction method is characterized by comprising the following steps of:
S1, collecting vehicle and environment information;
s2, analyzing and evaluating friction coefficient basic data;
s3, measuring and calculating the friction coefficient of the road surface;
s4, evaluating friction coefficient big data;
s5, friction coefficient early warning prediction;
in step S3, the road friction coefficient formula in the vehicle braking state is as follows:
a=a1+a2
wherein mu is the estimated friction coefficient of the road surface,
Mu 1 -friction coefficient between brake pad and brake disc,
F N, the unit is N,
R is the radius of the brake wheel, the unit is m,
R 1 -radius of brake block, unit is m,
M-the mass of the brake wheel, the unit is kg,
W, vehicle load, in N,
A, tire rotation deceleration, the unit is rad.s -2,
A 1, which is the tire rotation deceleration caused by wind resistance, the unit is rad.s -2,
A 2, the tire rotation deceleration caused by braking, the unit is rad.s -2,
F, wind resistance, wherein the unit is N;
In the step S4, the large data of the friction coefficient is evaluated, the average value of the friction coefficient in different states is calculated by collecting the estimated values of the friction coefficient of the road surface in different vehicles, speeds, positions, humidity and temperatures, a real-time evaluation model of the friction coefficient of the road surface is constructed by accumulating mass data, and the threshold value of the friction coefficient of the road surface in different states is designed.
2. The method for evaluating and predicting the friction coefficient of the road surface according to claim 1, wherein in S1, the vehicle and the environmental information are acquired in a sensing way, and the data information of the vehicle in the braking state process is monitored through a vehicle monitoring system and various sensors to obtain the related data of the vehicle and the road where the vehicle is located in the braking process.
3. The method for evaluating and predicting the friction coefficient of the pavement according to claim 1, wherein in S2, the basic data of the friction coefficient acquired in S1 is positioned and uploaded to the cloud platform, the data acquired by sensing vehicles at different positions are uploaded to the big data cloud platform through the internet of vehicles system, stored according to categories, and further processed, analyzed and fed back.
4. The method for estimating and predicting the friction coefficient of the road surface according to claim 1, wherein in S5, the friction coefficient early warning prediction gives a real-time vehicle friction coefficient threshold according to the real-time vehicle and the environmental conditions of the trip, and when the estimated value of the friction coefficient of the road surface in the running process of the vehicle exceeds the threshold, the big data cloud platform can remotely send an early warning signal to the vehicle through the wireless transmission module and provide a corresponding solution.
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| CN112884288A (en) * | 2021-01-25 | 2021-06-01 | 浙江公路水运工程咨询有限责任公司 | Highway rain and fog scene driving safety evaluation system |
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