CN109167818B - Road flatness detection system based on intelligent mobile phone crowdsourcing collection - Google Patents
Road flatness detection system based on intelligent mobile phone crowdsourcing collection Download PDFInfo
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Abstract
The invention discloses a road flatness detection system based on crowdsourcing acquisition of a smart phone, which comprises a data acquisition module, a data sending module and a cloud management center, wherein the data acquisition module, the data sending module and the cloud management center are arranged in the smart phone, the data acquisition module establishes a crowdsourcing task when a user opens electronic map navigation software, and the crowdsourcing task comprises the steps of acquiring a current longitudinal acceleration value and a current road inclination angle through a built-in sensor of the smart phone, and acquiring the position, the course, the time and the moving speed of a current road section where the user is located; the data sending module sends the crowdsourcing task to the cloud management center through a network; the cloud management center receives crowdsourcing tasks sent by different users, classifies and integrates various data in the crowdsourcing tasks, analyzes the integrated data through a road flatness detection algorithm, and evaluates the road quality. The invention improves the road flatness detection efficiency and the reliability of detection data, and meets the requirements of road side operation and side detection.
Description
Technical Field
The invention relates to the technical field of traffic information engineering and control, in particular to a road flatness detection system based on intelligent mobile phone crowdsourcing acquisition.
Background
At the present stage, comfort, safety and economy of travel are widely concerned, and road surface driving quality research shows that: the flatness characteristics of a road directly affect the driving quality of the road surface. From the analysis of a driving angle, the uneven road surface can increase the driving resistance, so that the vehicle generates additional vibration to cause bumping, influence the driving speed and safety, and easily cause fatigue of passengers or damage of goods; from the road angle analysis, the corresponding reaction force can be exerted on the road surface by bump and reaction of driving, and the uneven road surface can accumulate rainwater, so that the damage of the road surface is accelerated, and the service life and the maintenance period of the road are influenced. For example, in developed countries such as japan where the development of roads is fast, the road surface detection technology has started early and reaches a high level, automation has been achieved in the road flatness detection, and the data processing function of the detection device is relatively complete. However, the road surface detection technology of China starts late, and in the early stage of road career development, the attention on road flatness in the industry is insufficient, so that the development and implementation of the road surface detection technology in China are restricted. Therefore, the technology is still in the initial development stage in China, and part of detection technologies still have defects. The main detection modes at the present stage have the problems of complex process, complicated equipment, small data volume and poor timeliness.
Disclosure of Invention
The invention aims to provide a road flatness detection system based on crowdsourcing acquisition of a smart phone, which utilizes a crowdsourcing concept and takes a built-in sensor of the smart phone as a data source for road flatness detection so as to improve the road flatness detection efficiency and the reliability of detection data and meet the requirements of road side operation and road side detection.
In order to realize the task, the invention adopts the following technical scheme:
the road flatness detection system based on the crowdsourcing collection of the smart phone comprises a data collection module, a data sending module and a cloud management center, wherein the data collection module is arranged in the smart phone and is associated with electronic map navigation software installed on the smart phone;
the data acquisition module establishes a crowdsourcing task when a user opens the electronic map navigation software, wherein the crowdsourcing task comprises the steps of acquiring a current longitudinal acceleration value and a current road inclination angle through a built-in sensor of a mobile phone, and acquiring the position, the course, the time and the moving speed of a current road section where the user is located;
the data sending module sends the crowdsourcing task to the cloud management center through a network;
the cloud management center receives crowdsourcing tasks sent by different users, classifies and integrates various data in the crowdsourcing tasks, analyzes the integrated data through a road flatness detection algorithm, evaluates the road quality, and displays the road quality on the electronic map navigation software in a visual mode.
Further, the classifying and integrating various types of data in the crowdsourcing task includes:
the cloud management center divides the speed, and according to the position of the road section, the longitudinal acceleration value and the road inclination angle in each crowdsourcing task in the same speed interval of the road section are stored as a group of detection data list, and the course, the time and the moving speed corresponding to the group of detection data are stored in the list.
Further, after the classification and integration of various types of data in the crowdsourcing task, the compensation and judgment of the longitudinal acceleration value are also needed, and the method is that in each group of detection data:
when the road inclination is not 0, the longitudinal acceleration value:
Z'＝gc+g(1-cosθ)
in the above formula, gcThe longitudinal acceleration value is obtained through a built-in sensor of the mobile phone, g is the gravity acceleration, theta is the road inclination angle, and Z' is the longitudinal acceleration value after compensation.
Further, in the detection data, the road inclination angle is calculated by using an accelerometer and a gyroscope in a built-in sensor of the mobile phone and adopting a data fusion technology, and the specific method comprises the following steps:
(1) calculating the mobile phone inclination angle value corrected at the moment
Carrying out difference operation on a mobile phone inclination angle value alpha (n) acquired by an accelerometer in the gravity direction at the moment and a corrected mobile phone inclination angle value beta (n-1) calculated by the method in the step (1) at the previous moment, and multiplying the difference by a gain coefficient K to obtain a result, namely an error value epsilon of angle detection, wherein K is more than 0 and less than or equal to 1;
ε＝K·[β(n-1)-α(n)]
obtaining an angular velocity value omega (n) at the moment through a gyroscope, and then calculating a mobile phone inclination angle value beta (n) corrected at the moment through the following formula:
in the above formula, t (n) and t (n-1) respectively refer to the nth and the nth-1 time;
(2) calculating road inclination
The road inclination angle θ (n) at this time is calculated using the following formula:
θ(n)＝90°-β(n)
further, the road flatness detection algorithm includes:
(1) road flatness detection calibration
The method comprises the following steps of (1) utilizing a test vehicle to run at the speed in each speed interval on a road with known road quality, and utilizing an accelerometer and a gyroscope to detect the longitudinal acceleration value of the road; wherein the average value of longitudinal acceleration values obtained on a road with good quality grade is recorded as a low-amplitude threshold value ZlThe mean value of the longitudinal acceleration values obtained on roads of very poor quality class is the high amplitude threshold value Zh；
(2) Establishing a road flatness evaluation formula
When the longitudinal acceleration value meets the following relation, the road quality grade is judged to be good:
gc+g(1-cosθ)<Zl
when the longitudinal acceleration value meets the following relation, the road quality grade is judged to be extremely poor:
gc+g(1-cosθ)>Zh
the standard for evaluating a general road surface and a poor road surface is that when the following conditions are satisfied:
Zl<gc+g(1-cosθ)<Zh
if two continuous detection points of the longitudinal acceleration value on the time axis have peak values, the road grade is judged to be poor, otherwise, the road grade is judged to be general.
Further, the road quality is displayed in different colors on an electronic map of the electronic map navigation software, when a user clicks a road, the road quality and the detected times of the road are displayed in a pop-up window mode, and meanwhile, the longitudinal acceleration, the vehicle speed, the road inclination angle and the road position of the road are displayed above the electronic map.
Compared with the prior art, the invention has the following technical characteristics:
1. the system of the invention can be integrated into the existing electronic navigation map software, and appears as a sub-module thereof, and the main detection equipment is a smart phone of a navigation user. Road flatness detection is carried out while the user navigates, and the work can be finished without organizing a professional detection team, so that the detection cost is greatly reduced, the detection data timeliness is high, and the work efficiency of road flatness detection is also improved.
2. The method and the device utilize the crowdsourcing concept to process the road flatness detection data, utilize the data detected by all navigation users to correct and update the road flatness evaluation grade of the corresponding road at any time, have large data volume, finally trend the detection result, naturally remove the data with larger errors, and greatly improve the accuracy of the detection result.
3. The road flatness detection algorithm is relatively simple, the current road quality evaluation grade can be obtained without a large amount of data preprocessing and processing, and the load of a server is reduced.
Drawings
FIG. 1 is a flow chart of the operation of the system of the present invention;
FIG. 2 is a schematic view of the longitudinal acceleration of a vehicle at a road inclination of 0;
FIG. 3 is a schematic view of the longitudinal acceleration of the vehicle at a road inclination angle other than 0;
FIG. 4 is a flow chart of an accelerometer and gyroscope data fusion algorithm;
FIG. 5 is a schematic diagram of high amplitude threshold, low amplitude threshold and road quality grading;
FIG. 6 is a schematic view of a road grade determination;
FIG. 7 is a schematic diagram of a test result interface according to an embodiment of the present invention.
Detailed Description
In the big data age, map navigation software has become essential travel software, and the user volume of the high-grade map and the hectometrical map has been reported to break through 10 hundred million. Most of smart phones are embedded with sensors such as a GPS (global positioning system), an acceleration sensor and a gyroscope, can collect various data of human activities and surrounding environments, implicitly contains important information such as basic attributes of road flatness, and provides a data source for automatic detection of road flatness. The system is integrated in the existing navigation map software, so that a large number of data acquisition sources can be formed, an internet +' service which accords with a crowdsourcing idea is formed, the requirement of detecting the operation of a road is met, and the road flatness detection efficiency and the detection data reliability are improved.
The invention discloses a road flatness detection system based on crowdsourcing acquisition of a smart phone, which comprises a data acquisition module, a data sending module and a cloud management center, wherein the data acquisition module is arranged in the smart phone and is associated with electronic map navigation software installed on the smart phone;
the data acquisition module is arranged in the smart phone, can be a functional module/hardware module of the smart phone, and can also be a functional module of the navigation software. The navigation software adopts products mature in the market at present, such as God, Baidu and the like. The data sending module can be an independent functional module and can also be a wireless communication module of the smart phone. The cloud management center comprises a cloud server for data processing. The smart phone is a phone loaded with an operating system such as android or IOS; the association means that the acquisition module can communicate with the navigation software. The system of the invention has the following working procedures:
namely, the data acquisition module activates and establishes a crowdsourcing task only when the user opens the navigation software, and the task is to acquire data by calling a built-in sensor of the mobile phone. The related mobile phone built-in sensor comprises: the system comprises an accelerometer, a gyroscope and a GPS module, wherein the position, the course, the time and the moving speed are obtained through the GPS module, the longitudinal acceleration value is obtained through the accelerometer, and the road inclination angle is obtained through the gyroscope. Since the road flatness detection is mainly performed by the vehicle at a certain moving speed, the user in the scheme refers to a user who gets on or drives the vehicle, and the position and the speed refer to the position or the speed of the vehicle where the user gets on or drives the vehicle currently.
and the data sending module sends the crowdsourcing task to a cloud management center for calculation through a wired or wireless network. Due to the adoption of the crowdsourcing idea, the cloud management center can continuously acquire crowdsourcing task data sent by a large number of users, different road sections and different time.
Step 3, the cloud management center receives crowdsourcing tasks sent by different users, classifies and integrates various data in the crowdsourcing tasks, analyzes the integrated data through a road flatness detection algorithm, evaluates road quality, and displays the road quality on the electronic map navigation software in a visual mode, wherein the specific steps are as follows:
step 3.1, the cloud management center receives crowdsourcing tasks sent by different users, and classifies and integrates various data in the crowdsourcing tasks
After each user opens the navigation software, the acquired crowdsourcing data is sent to the cloud management center at intervals, the data sent each time is a crowdsourcing task, and the task comprises data including longitudinal acceleration values, road inclination angles, positions, headings, time and moving speeds.
When detecting the road flatness, the main parameters to be considered are speed, longitudinal acceleration value and road inclination. Since the vehicle runs at different speeds on the same road surface, the longitudinal acceleration of the vehicle is different, and the comparison of the detection data belonging to the same speed zone is more scientific, the system needs to process the running speed of the vehicle in zones, for example, the running speed of the vehicle is processed in zones by taking 60km/h as a starting point and 10km/h as a basic interval, namely 60km/h-70km/h, 70km/h-80km/h and the like, and the upper limit is 120 km/h. All data in the same interval are stored as a list, so that the subsequent display and processing processes are facilitated.
The cloud management center partitions the speed, stores longitudinal acceleration values and road inclination angles in each crowdsourcing task in the same speed interval of the road section as a group of detection data list according to the position of the road section, and stores the course, time and moving speed corresponding to the group of detection data in the list, as shown in table 1.
TABLE 1
For example, in table 1, user 1 and user 2 respectively send crowdsourcing tasks, and through analysis of data in the crowdsourcing tasks, the vehicle speeds of user 1 and user 2 are 65km/h and 68km/h respectively, and the current positions of both people obtained through the GPS module are located in the section 1, so that the data in the crowdsourcing tasks of both people are stored in the section 60km/h-70km/h of the section 1 in table 1; for the user 1, the longitudinal acceleration value and the road inclination angle in the crowdsourcing task sent by the user are used as a group of detection data, and the course, the time and the moving speed corresponding to the group of detection data are stored in the following steps: in the "other data" column.
Therefore, the roads, the speed sections and the data sent by the users can be associated through the lists, and large-scale and efficient data calculation is facilitated. Table 1 may further be refined, that is, the road segment 1 is further divided into each cell, and then, in the crowdsourcing data sent by the user, by analyzing the position of the current road segment of the user, data in the crowdsourcing task may be further divided into smaller road segment intervals, and the finer the division is, the more accurate the detection result is.
The classification and integration process realizes the classification and integration of a large amount of collected user data.
In order to make the detection result more accurate, the invention adopts the following method to ensure the accuracy and effectiveness of the data:
in each set of detection data, the error of the longitudinal acceleration value obtained at the detection point (namely, the longitudinal acceleration value obtained at the road inclination angle acquisition moment) can be corrected through the road inclination angle, so that the deviation error of the evaluation result is eliminated. In the road surface detection process in which the road inclination exists, the vehicle longitudinal acceleration directly detected is divided into longitudinal accelerations that are smaller than the actual longitudinal acceleration, and therefore, when the road quality evaluation result is a high-amplitude threshold value, a deviation in the evaluation result may occur.
The vehicle stress analysis shows that: the longitudinal acceleration of the vehicle at this time is perpendicular to the vehicle and equal to the gravitational acceleration g of the vehicle, as shown in fig. 2, and the longitudinal acceleration value Z at this time is calculated by the formula:
Z＝gc＝g
wherein, gcIs the longitudinal acceleration directly detected by the accelerometer, g is the gravitational acceleration; it is clear that the longitudinal acceleration in this case is directly measurable, i.e. the final longitudinal acceleration value is the detected value, without compensation. When the vehicle runs at no road inclination angle, the vehicle suddenly presses a pothole on the road surface to cause the vehicle to jolt temporarily, the process can be simplified into that at the moment, the supporting force of the vehicle on the road surface is increased, the longitudinal acceleration is also increased, and the calculation formula of the longitudinal acceleration at the moment is as follows:
Z＝gc＝g+a
wherein a is the longitudinal acceleration due to the bump; it is obvious that the longitudinal acceleration values in this case are also directly measurable. Since the longitudinal acceleration detected by the vehicle is perpendicular to the vehicle, in the case of a slope, the directly detected longitudinal acceleration is different from its actual value, and the gravitational acceleration acts in the form of component acceleration on the longitudinal axis direction of the vehicle, giving the vehicle longitudinal acceleration. In the ideal case of no jerk, the directly detected longitudinal acceleration gcCorrections are needed as shown in fig. 3.
When the road inclination angle in a certain set of detection data is not 0, the longitudinal acceleration value in the set of detection data needs to be compensated:
Z'＝gc+g(1-cosθ)
in the above formula, gcThe longitudinal acceleration value obtained by a built-in sensor of the mobile phone is g gravity acceleration, theta is a road inclination angle, and Z' is the longitudinal acceleration value after compensation, and the value before compensation is replaced by the value.
Further, in the detection data, the road inclination angle is calculated by using an accelerometer and a gyroscope in a built-in sensor of the mobile phone and adopting a data fusion technology, and the specific method comprises the following steps:
(1) calculating the mobile phone inclination angle value corrected at the moment
The mobile phone inclination angle value alpha (n) acquired by the accelerometer in the gravity direction has deviation from an actual value, so the step corrects the value alpha (n) acquired by the mobile phone, and a corrected accurate mobile phone inclination angle value is obtained:
and (2) carrying out difference operation on the mobile phone inclination angle value alpha (n) acquired by the accelerometer in the gravity direction at the moment and the corrected mobile phone inclination angle value beta (n-1) calculated by the method in the step (1) at the last moment, and multiplying the difference by a gain coefficient K to obtain a result, namely an error value epsilon of angle detection:
ε＝K·[β(n-1)-α(n)]
obtaining an angular velocity value omega (n) at the moment through the gyroscope, obtaining the measured angular value through integral operation, and adding the angular velocity value omega (n) with an error value epsilon of angle detection obtained by the accelerometer at the moment to realize the correction of the angular value of the gyroscope at the moment, thereby obtaining a more accurate mobile phone inclination angle value beta (n) after the correction at the moment:
in the above formula, t (n) and t (n-1) are the nth and the nth-1 time, respectively.
The algorithm effectively inhibits the detection errors of the accelerometer and the gyroscope, so that the detection result is more accurate. Wherein, the value range of the gain coefficient K is as follows: k is more than 0 and less than or equal to 1; the magnitude of K depends on the length of the detection time, and the larger the detection time, the smaller the value of K, and conversely, the larger the value of K, the initial value, i.e., the maximum value is 1, as shown in fig. 4.
(2) Calculating road inclination
Obtaining the inclination angle value of the mobile phone corrected at the moment through the calculation of the previous step, and then calculating the road inclination angle theta (n) at the moment by using the following formula:
θ(n)＝90°-β(n)
step 3.2, analyzing the integrated data through a road flatness detection algorithm and evaluating the road quality
After the integrated data are obtained, the integrated data are analyzed by formulating a road flatness detection algorithm, so that the road quality is obtained and visually displayed, and the method specifically comprises the following steps:
(1) road flatness detection calibration
Prior to use of the system of the present invention, experimental analysis was required to determine the road grading criteria of the system.
The method comprises the following steps of (1) utilizing a test vehicle to run at the speed in each speed interval on a road with known road quality, and utilizing an accelerometer and a gyroscope to detect the longitudinal acceleration value of the road; wherein the average value of longitudinal acceleration values obtained on a road with good quality grade is recorded as a low-amplitude threshold value ZlThe mean value of the longitudinal acceleration values obtained on roads of very poor quality class is the high amplitude threshold value Zh。
The experimental process in the invention is to determine the low amplitude threshold and the high amplitude threshold, so that only two road surfaces with known road quality need to be selected for experiment. For example, for a road with good known quality, firstly, a test vehicle is used for driving in an interval of 60km/h-70km/h, the longitudinal acceleration value of the road is collected in the process, and correction is carried out when the inclination angle of the road is not zero; averaging longitudinal acceleration values acquired at all times on the road to obtain a low-amplitude threshold value Zl. By the same principle, a region of 60km/h-70km/h can be obtainedHigh amplitude threshold Z at intervalshAs shown in fig. 5.
(2) Establishing a road flatness evaluation formula
When the road flatness is detected, one part is the detection of good and extremely poor road flatness quality, and the other part is the detection of common and poor road flatness quality. Namely, the road flatness is divided into four conditions of good, general, poor and extremely poor, as shown in fig. 6.
When the longitudinal acceleration value meets the following relation, the road quality grade is judged to be good:
gc+g(1-cosθ)<Zl
when the longitudinal acceleration value meets the following relation, the road quality grade is judged to be extremely poor:
gc+g(1-cosθ)>Zh
the standard for evaluating a general road surface and a poor road surface is that when the following conditions are satisfied:
Zl<gc+g(1-cosθ)<Zh
in actual detection, longitudinal acceleration values detected at different times can be connected into a curve on a time axis. If the longitudinal acceleration value has two continuous detection points on the time axis and has peak values (the longitudinal acceleration value exceeds a high-amplitude threshold value), judging that the road grade is poor, otherwise, judging that the road grade is general.
Preferably, when this step evaluates, the longitudinal acceleration value for this step is an average value of all longitudinal acceleration values acquired by the road in the current speed interval. For example, 100 sampling moments are provided in the interval of 60km/h-70km/h (all users provide crowd-sourced task data to the cloud management center at fixed sampling intervals), and the average value of the longitudinal acceleration values provided by all users at each moment is used as the longitudinal acceleration value at the moment. Since the data is dynamically changing, the road quality may change as the user data increases, with more data and more accurate results.
Step 3.3, displaying the road quality on the electronic map navigation software in a visual mode
The road quality is displayed in different colors on an electronic map of electronic map navigation software, when a user clicks the road, the road quality and the detected times (namely the number of users participating in data providing) of the road are displayed in a pop-up window mode, and meanwhile, the longitudinal acceleration (the average value of all users), the vehicle speed (the average value of all users), the road inclination angle (the average value of all users) and the road position (acquired through a GPS module) of the road are displayed above the electronic map.
For the flatness of a section of road, the corresponding road quality in the speed-limiting section of the road is generally calculated. For example, for a section of road with the speed limit of 60km/h-80km/h, firstly, all data of 60km/h-70km/h interval and 70km/h-80km/h interval are respectively obtained through the table 1, the 60km/h-70km/h interval and the 70km/h-80km/h interval are respectively obtained, averaging the longitudinal acceleration values of all the users on the road at each sampling moment, averaging the two average values corresponding to each sampling moment, and obtaining the final longitudinal acceleration value at each sampling moment in the speed-limiting interval of the road, so as to obtain the curve of the longitudinal acceleration value of the road along with the change of time, then two low amplitude threshold values Z corresponding to the interval of 60km/h-70km/h and the interval of 70km/h-80km/h are used.lTwo high amplitude thresholds ZhThe average values are obtained as the low amplitude threshold value and the high amplitude threshold value in the speed-limited section, and the evaluation is performed in step 3.2 (2), and the results are displayed on the electronic map.
In order to facilitate visual distinction of the quality of the detected road, the road is distinguished by different colors: well: green; in general: yellow; difference: blue; extremely poor: red. The result presentation mode is similar to the mode of displaying the congestion result in the electronic navigation map navigation software, and lines with different colors are directly superimposed on the roads in the map, as shown in fig. 7, which is a schematic diagram of the actual display result of the embodiment of the present invention. In addition, a road section searching function can be added, the specified road section can be directly searched, and road flatness detection data of the road section at the moment can be provided for a user; and the result carries out data sharing along with the navigation user, and the detection result is updated in real time.
Claims (3)
1. The road flatness detection system based on the crowdsourcing collection of the smart phone is characterized by comprising a data collection module, a data sending module and a cloud management center, wherein the data collection module is arranged in the smart phone and is associated with electronic map navigation software installed on the smart phone;
the data acquisition module establishes a crowdsourcing task when a user opens the electronic map navigation software, wherein the crowdsourcing task comprises the steps of acquiring a current longitudinal acceleration value and a current road inclination angle through a built-in sensor of a mobile phone, and acquiring the position, the course, the time and the moving speed of a current road section where the user is located;
the data sending module sends the crowdsourcing task to the cloud management center through a network;
the cloud management center receives crowdsourcing tasks sent by different users, classifies and integrates various data in the crowdsourcing tasks, analyzes the integrated data through a road flatness detection algorithm, evaluates the road quality, and displays the road quality on the electronic map navigation software in a visual mode;
the classification and integration of various data in the crowdsourcing task comprises the following steps:
the cloud management center partitions the speed, stores longitudinal acceleration values and road inclination angles in each crowdsourcing task in the same speed interval of the road section as a group of detection data list according to the position of the road section, and stores the course, time and moving speed corresponding to the group of detection data in the list;
after the classification and integration of various types of data in the crowdsourcing task, the compensation judgment of the longitudinal acceleration value is needed, and the method comprises the following steps of:
when the road inclination is not 0, the longitudinal acceleration value:
Z'＝gc+g(1-cosθ)
in the above formula, gcG is the gravity acceleration and theta is the longitudinal acceleration value obtained by a built-in sensor of the mobile phoneThe road inclination angle Z' is a longitudinal acceleration value after compensation;
in the detection data, the road inclination angle is calculated by using an accelerometer and a gyroscope in a built-in sensor of the mobile phone by adopting a data fusion technology, and the specific method comprises the following steps:
(1) calculating the mobile phone inclination angle value corrected at the moment
Carrying out difference operation on a mobile phone inclination angle value alpha (n) acquired by an accelerometer in the gravity direction at the moment and a corrected mobile phone inclination angle value beta (n-1) calculated by the method in the step (1) at the previous moment, and multiplying the difference by a gain coefficient K to obtain a result, namely an error value epsilon of angle detection, wherein K is more than 0 and less than or equal to 1;
ε＝K·[β(n-1)-α(n)]
obtaining an angular velocity value omega (n) at the moment through a gyroscope, and then calculating a mobile phone inclination angle value beta (n) corrected at the moment through the following formula:
in the above formula, t (n) and t (n-1) respectively refer to the nth and the nth-1 time;
(2) calculating road inclination
The road inclination angle θ (n) at this time is calculated using the following formula:
θ(n)＝90°-β(n)。
2. the road flatness detection system based on smartphone crowdsourcing acquisition as claimed in claim 1, wherein the road flatness detection algorithm comprises:
(1) road flatness detection calibration
The method comprises the following steps of (1) utilizing a test vehicle to run at the speed in each speed interval on a road with known road quality, and utilizing an accelerometer and a gyroscope to detect the longitudinal acceleration value of the road; wherein the average value of longitudinal acceleration values obtained on a road with good quality grade is recorded as a low-amplitude threshold value ZlThe mean value of the longitudinal acceleration values obtained on roads of very poor quality class is the high-amplitude thresholdValue Zh；
(2) Establishing a road flatness evaluation formula
When the longitudinal acceleration value meets the following relation, the road quality grade is judged to be good:
gc+g(1-cosθ)<Zl
when the longitudinal acceleration value meets the following relation, the road quality grade is judged to be extremely poor:
gc+g(1-cosθ)>Zh
the standard for evaluating a general road surface and a poor road surface is that when the following conditions are satisfied:
Zl<gc+g(1-cosθ)<Zh
if two continuous detection points of the longitudinal acceleration value on the time axis have peak values, the road grade is judged to be poor, otherwise, the road grade is judged to be general.
3. The road flatness detection system based on the smart phone crowdsourcing collection as claimed in claim 1, wherein the road quality is displayed in different colors on an electronic map of electronic map navigation software, when a user clicks a road, the road quality and the detected times of the road are displayed in a pop-up window manner, and the longitudinal acceleration, the vehicle speed, the road inclination angle and the road position of the road are displayed above the electronic map.
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