WO2019080380A1 - 轮迹横向分布测量系统及测量方法 - Google Patents

轮迹横向分布测量系统及测量方法

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Publication number
WO2019080380A1
WO2019080380A1 PCT/CN2018/072785 CN2018072785W WO2019080380A1 WO 2019080380 A1 WO2019080380 A1 WO 2019080380A1 CN 2018072785 W CN2018072785 W CN 2018072785W WO 2019080380 A1 WO2019080380 A1 WO 2019080380A1
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WO
WIPO (PCT)
Prior art keywords
vehicle
data
horizontal section
wheel
distance
Prior art date
Application number
PCT/CN2018/072785
Other languages
English (en)
French (fr)
Inventor
麦德荣
范向晨
杨方剑
Original Assignee
深圳大学
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Filing date
Publication date
Application filed by 深圳大学 filed Critical 深圳大学
Publication of WO2019080380A1 publication Critical patent/WO2019080380A1/zh
Priority to US16/600,426 priority Critical patent/US11208101B2/en

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/10—Path keeping
    • B60W30/12—Lane keeping
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C15/00—Surveying instruments or accessories not provided for in groups G01C1/00 - G01C13/00
    • G01C15/002—Active optical surveying means
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
    • B60W40/06—Road conditions
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0062—Adapting control system settings
    • B60W2050/0075—Automatic parameter input, automatic initialising or calibrating means
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2420/00—Indexing codes relating to the type of sensors based on the principle of their operation
    • B60W2420/40—Photo, light or radio wave sensitive means, e.g. infrared sensors
    • B60W2420/403—Image sensing, e.g. optical camera
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2420/00—Indexing codes relating to the type of sensors based on the principle of their operation
    • B60W2420/40—Photo, light or radio wave sensitive means, e.g. infrared sensors
    • B60W2420/408—Radar; Laser, e.g. lidar
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2552/00—Input parameters relating to infrastructure
    • B60W2552/35—Road bumpiness, e.g. potholes
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2556/00—Input parameters relating to data
    • B60W2556/10—Historical data
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2754/00—Output or target parameters relating to objects
    • B60W2754/10—Spatial relation or speed relative to objects

Definitions

  • the invention belongs to the technical field of transportation engineering and highway roadbed pavement, and particularly relates to a wheel track lateral distribution measuring system and a measuring method.
  • the characteristics of the lateral distribution of the wheel tracks in different regions are different, which highlights the importance of the uneven design of the road structure.
  • the significance of studying the lateral distribution of the wheel tracks is that the wheel lateral distribution coefficient can objectively describe the vehicle wheel track laterally.
  • the distribution characteristics of the location are of great significance for the accurate analysis of the mechanical structure of the pavement.
  • the current main method for measuring the lateral distribution characteristics of the wheel track is the on-site traffic photography video method.
  • This method requires the traffic safety department and the road management department to cooperate with 4-5 surveyors to draw a certain scale on the road surface after the road is closed. After the road, the position of the ruler is pressed by the camera when the wheel passes. After the road shooting is finished, the surveyor records the video by watching the video recording.
  • the significant disadvantages are: a large amount of labor and a large amount of time, which are generally staged and short-lived.
  • the invention provides a new automatic, all-weather unattended wheel track lateral distribution measuring system and method capable of automatically recognizing a vehicle and an automatic sub-vehicle speed measurement, which not only creates a vehicle and wheel identification, but also detects vehicle speed detection. Conditions; adapt to the needs of automation, and the requirements for efficiency, efficiency, precision and safety in research and social production. Throughout the world's research on the horizontal distribution characteristics of the wheel tracks, some foreign researchers have carried out the actual measurement of the lateral distribution of the wheel track, and obtained typical values applicable to the local situation. However, the horizontal distribution coefficient of the wheel track in China still stays in the national statistics.
  • the invention and experiments of this project are conducive to the local organization to carry out the lateral distribution observation of the wheel track, especially the horizontal distribution observation of the wheel track and the local distribution of the wheel track lateral distribution data, which can significantly enhance the regional Subgrade pavement structure design accuracy.
  • the present invention provides a new and automated wheel track lateral distribution measuring system and measuring method, which realizes automatic detection of vehicle type and wheel classification, traffic investigation, vehicle speed detection by means of vehicles, and lateral distribution of road wheel tracks. .
  • the measurement accuracy of the system has been greatly improved, the efficiency is high, unattended, and all-weather data collection can be realized.
  • a wheel track lateral distribution measuring system including a distance measuring system of a horizontal section of a vehicle to a shoulder, a shape and size characteristic database of a horizontal section side of a vehicle, and a wheel track.
  • Horizontal distribution feature determination system and log track lateral distribution feature database is:
  • the distance measurement system of the horizontal section of the vehicle to the shoulder determines the shape and size characteristic data of the horizontal section of the vehicle and the distance data from the side of the horizontal section of the vehicle to the shoulder, and saves the data to the shape and size of the horizontal section of the vehicle.
  • the horizontal profile side shape and size characteristic database of the vehicle is used for storing the shape, size characteristic data of the measured horizontal section side of the sample vehicle, the distance data from the side of the horizontal section of the vehicle to the shoulder, and the horizontal section of the wheel of the original input of the system. Shape and size characteristic data of one side;
  • the wheel track lateral distribution characteristic measuring system analyzes the shape and size characteristic data of one side of the horizontal section of the sample vehicle and the distance data from one side of the sample vehicle horizontal section to the shoulder of the road and finally determines the lateral distribution characteristics of the wheel track;
  • the wheel track lateral distribution feature database is used for storing wheel track lateral distribution feature data, and can be used to simulate the wheel track lateral distribution characteristic frequency model, determine and output the wheel track lateral distribution coefficient, and then study the vehicle load on the road surface for a long time. Damage impact.
  • the distance measuring system of the horizontal section of the vehicle to the shoulder comprises an embedded development board and two ultra-high frequency laser ranging sensors, and the two ultra-high frequency laser ranging sensors are in the driving direction.
  • the roadside is placed on the horizontal line of the same height and works simultaneously to save the data to the shape and size feature database on one side of the horizontal section of the vehicle.
  • the distance measurement system of the horizontal section of the vehicle has a preliminary screening, data processing and output data; the preliminary screening means that only valid sample data within a certain target is selected; the data processing refers to calculation
  • the vehicle speed V is fitted to the original data collected by the two ultra-high frequency laser ranging sensors, and at the same time, the data is converted into the distance association length (DL) form by the distance correlation time (DT), that is, the reduction
  • the actual shape and size characteristic data on one side of the horizontal section of the vehicle are outputted to the shape and size characteristic database on one side of the horizontal section of the vehicle.
  • the shape and size characteristic database on one side of the horizontal section of the vehicle includes three parts: A zone, B zone and C zone, and zone A is used for storing the vehicle level measured by the distance measuring system of the vehicle horizontal section side to the shoulder.
  • the effective sample data of the shape, size feature and the distance from one side of the horizontal section of the vehicle to the shoulder of the vehicle, that is, the target vehicle database is used to store the shape and size characteristic data of the horizontal section of the wheel of the original input of the database, that is, the original Wheel database
  • Area C is used to store the shape and size characteristic data of the horizontal section of the sample wheel selected from the A area, that is, the target wheel database; the data of the B area can be automatically added to the data of the C area for automatic updating;
  • Area A and B The area data is available for the wheel track lateral distribution feature determination system call.
  • the method for measuring the lateral distribution of the wheel track includes the following steps:
  • the distance measurement system of the horizontal section of the vehicle to the shoulder determines the shape and size data of the horizontal section of the vehicle and the distance data of the horizontal section of the vehicle to the laser ranging sensor, which is the laser ranging
  • the section of the vehicle erecting height parallel to the ground cuts the section obtained by the vehicle, and the distance measuring system of the horizontal section of the vehicle to the shoulder includes two superimposed horizontal lines at the same height of the roadside along the driving direction.
  • Vehicle identification Identify complete vehicle data, which refers to all voltage analog signal data obtained by the distance measuring system when a sample vehicle passes through a distance measuring system from one side of the vehicle horizontal section to the shoulder;
  • Speed calculation Calculate the real speed of the vehicle, then add the time interval between the two sensors to obtain the signal, convert the voltage analog signal data into length data, restore the true shape and size characteristic data of the horizontal section of the vehicle, and pass the vehicle.
  • the number of axles and the length of the vehicle are identified by the vehicle, and then stored in the shape and size characteristic database on the side of the horizontal section of the vehicle together with the distance data of the horizontal section of the vehicle to the laser ranging sensor as the target vehicle database;
  • the wheel track lateral distribution feature measurement system retrieves the similarity between the target vehicle database and the original wheel database from the shape and size feature database on the horizontal section side of the vehicle, and then selects the target wheel from the target vehicle database. And stored in the target wheel database, the target wheel data content includes the shape and size of one side of the horizontal section of the wheel and the distance from the side of the horizontal section of the wheel to the laser;
  • the method for identifying the vehicle in step 2) is: determining whether the number of neutrals between consecutive points in the voltage analog signal data is greater than a certain value, performing preliminary screening of the target vehicle and dividing the data into segments, and then calculating the data according to each segment. A certain range of neutrals is grouped, and the distance between the data points and the laser ranging sensor is combined to determine whether each group of data is the same vehicle.
  • the Pearson correlation calculation is performed on the two columns of data obtained by the dual sensors when the same vehicle passes, and the correlation coefficient is considered to be the calculated speed when the correlation coefficient is greater than 0.85;
  • the speed calculation method of the step 3) is: the level of the same wheel One side of the profile needs to be scanned by two UHF laser ranging sensors, and then the horizontal distance S between the two sensors and the time difference ⁇ t of the first data are used to calculate the traveling speed V of the vehicle, and then the data is The form of the distance association time (DT) is converted into the form of the distance association length (DL), that is, the true shape and size feature data on the side of the horizontal section of the vehicle is restored, and output to the shape and size feature database of the horizontal section side of the vehicle.
  • DL distance association length
  • the target vehicle data is two-dimensional data
  • the horizontal axis is the length
  • the vertical axis is the distance.
  • the time interval of the signal acquired by the dual sensor is multiplied by the speed of the vehicle to restore the true shape and size data of the horizontal section side of the vehicle and is performed. Fit, and then correlate the distance from the horizontal section of the vehicle to the distance of the laser ranging sensor.
  • the original wheel data is two-dimensional data
  • the horizontal axis is the length
  • the vertical axis is the distance.
  • the method of acquiring the target vehicle is adopted, and only the wheel is measured.
  • the screening method of the target wheel in step 5) is to perform Pearson correlation calculation and Pearson correlation coefficient one by one from each end of the original wheel database from the back end of any data in the target vehicle database.
  • it is greater than 0.95, a certain segment of the column vehicle data is intercepted and stored in the target wheel data; the target wheel data is two-dimensional data, the horizontal axis is the length, and the vertical axis is the distance.
  • the invention can calculate the law of the lateral distribution coefficient of the wheel track under different conditions, and can investigate the influence of factors such as different time periods, vehicle speed, vehicle density, lane width and vehicle composition on the distribution coefficient.
  • the present invention has two high-precision, ultra-high-frequency laser ranging sensors working together, and fitting the measured data, which can ensure and greatly improve the efficiency and accuracy of the lateral distribution observation of the wheel track, and can
  • the observation data is initially selected and stored, and the stored data includes the wheel track lateral distribution data and the shape and size characteristic data on one side of the horizontal section of the wheel.
  • Figure 1 is a front view showing the layout of a wheel track lateral distribution measuring system of the present invention
  • Figure 2 is a left side view of Figure 1;
  • Figure 3 is a schematic view showing the horizontal cutting position of the wheel
  • Figure 4 is a schematic view showing the characteristic curve of the "convex concave and convex" of the wheel when the laser is swept over the horizontal section of the wheel;
  • Figure 5 is a schematic diagram of the shape of one side of the horizontal section of the wheel (time/distance) when the dual sensor is scanned;
  • Figure 6 is a schematic view (length/distance) of the true shape and size of the horizontal side of the data reduction wheel
  • Figure 7 is a schematic diagram of the distance from the side of the horizontal section of the wheel to the shoulder of the horizontal section of the wheel after fitting (length/distance);
  • Figure 8 is a flow chart of a method for measuring the lateral distribution of the wheel track of the present invention.
  • the invention is described by taking a six-axle truck as an example.
  • the wheel track lateral distribution measuring system of the present invention comprises a distance measuring system for one side of the vehicle horizontal section to the shoulder of the road, a shape and size characteristic database of the horizontal section side of the vehicle, a wheel track lateral distribution characteristic measuring system and a wheel track lateral distribution characteristic database.
  • the distance measurement system from one side of the horizontal section of the vehicle measures the shape and size characteristic data of the horizontal section of the vehicle and the distance data from the side of the horizontal section of the vehicle to the shoulder, and saves the data to the shape and size characteristic database of the horizontal section of the vehicle. ;
  • a horizontal shape and size characteristic database of the horizontal section of the vehicle for storing the shape, size characteristic data of the side of the horizontal section of the sample vehicle, the distance data from the side of the horizontal section of the vehicle to the shoulder, and the side of the horizontal section of the wheel of the original input of the system Shape and size characteristic data;
  • the wheel track lateral distribution characteristic measuring system analyzes the shape and size characteristic data of one side of the horizontal section of the sample vehicle and the distance data from the side of the sample vehicle horizontal section to the shoulder of the road and finally determines the lateral distribution characteristics of the wheel track;
  • the wheel track lateral distribution feature database is used to store the wheel track lateral distribution feature data, and can be used to simulate the wheel track lateral distribution characteristic frequency model, determine and output the wheel track lateral distribution coefficient, and then study the influence of vehicle load on the road surface long-term damage. .
  • the distance measurement system from one side of the horizontal section of the vehicle to the shoulder includes an embedded development board and two UHF laser ranging sensors. It also has independent power and memory with data input, output and storage functions.
  • the six-axle truck has six sets of wheels 2 and axles 3.
  • Two ultra-high-frequency laser ranging sensors are placed along the driving direction on the horizontal line at the same height of the roadside.
  • the laser light 5 is perpendicular to the car.
  • the direction of travel, the vertical height of the laser emission point from the road surface (H) is the common height of the bumps on the wheel hub of the truck.
  • H There are many types of truck wheels, so H has a certain range of values.
  • the horizontal cutting position of the wheel is measured by laser.
  • the profile obtained by the vehicle is cut from the plane parallel to the ground, and the laser emission point is located at the shoulder of one side of the road cross section.
  • the two laser ranging sensors work simultaneously to save the data to the horizontal section side of the vehicle. Database of shape and size characteristics.
  • the present invention only needs to distinguish between the two-wheel load-bearing axle of the heavy-duty truck and other types of axles.
  • Heavy-duty truck load-bearing axles need to carry a large weight, so the configuration of a single-sided two-wheel set is generally adopted.
  • the horizontal section of the wheel has a convex portion 6 and a concave portion 7 due to the presence of the axle 3, and the contour line features a shape of "convex concave and convex".
  • the inner wheel is concave inward and the outer wheel is facing outward, so that the outer tire is also connected to the axle.
  • the data form is the voltage analog signal of the time-related distance, and the distance is the distance from the horizontal section of the vehicle to the laser ranging sensor. , save the data.
  • the distance measurement system from the horizontal section of the vehicle to the shoulder is used for preliminary screening, data processing and output; preliminary screening means that only valid sample data within a certain target is selected; data processing refers to calculating the vehicle speed V by
  • the original data collected by the two UHF laser ranging sensors is fitted and processed, and the data is converted from the distance correlation time (DT) into the distance correlation length (DL), that is, the vehicle horizontal profile is restored.
  • the real shape and size feature data of the side are output to the shape and size feature database on one side of the horizontal section of the vehicle.
  • FIG. 5 is a schematic diagram showing the shape of one side of the horizontal section of the wheel when the two sensors are scanned, and the data is in the form of distance correlation time (DT);
  • FIG. 5 is a schematic diagram showing the shape of one side of the horizontal section of the wheel when the two sensors are scanned, and the data is in the form of distance correlation time (DT);
  • Fig. 6 is a schematic diagram of the true shape and size of the horizontal side of the data reduction wheel, and the data is the distance correlation length (DL). form.
  • Fig. 7 is a schematic diagram showing the distance from one side of the horizontal section of the horizontal section of the wheel to the shoulder distance of the horizontal section of the wheel after fitting, and the data is in the form of the distance-related length (D-L).
  • the shape and size characteristic database on one side of the horizontal section of the vehicle includes three parts: A zone, B zone and C zone.
  • Area A is used to store the shape of the horizontal section side of the vehicle measured by the distance measurement system from one side of the horizontal section of the vehicle to the shoulder.
  • the B zone is used to store the shape and size characteristic data of the horizontal section of the wheel of the original input of the database, ie the original wheel database; It is used to store the shape and size characteristic data of the horizontal section of the sample wheel selected from the A area, that is, the target wheel database; the data of the B area can be automatically updated by the data of the C area; the data of the A area and the B area are available.
  • Wheel track lateral distribution feature determination system call is used to store the shape and size characteristic data of the horizontal section of the wheel of the original input of the database, ie the original wheel database; It is used to store the shape and size characteristic data of the horizontal section of the sample wheel selected from the A area, that is, the target wheel database; the data of the B area can be automatically updated by the data of the C area; the data of the A area and the B area are available. Wheel track lateral distribution feature determination system call.
  • the method for measuring the lateral distribution of the wheel track comprises the following steps:
  • the distance measurement system of the horizontal section of the vehicle to the shoulder determines the shape and size data of the horizontal section of the vehicle and the distance data from the horizontal section of the vehicle to the laser;
  • Vehicle identification Identify the complete vehicle data, that is, identify the complete voltage analog signal data from the data.
  • the specific method is as follows: the data obtained by the dual sensors are processed simultaneously, if the voltage is analog between the continuous points in the analog signal data If the number is greater than a certain value X, the data is divided into several segments, and each segment of data is grouped according to a certain neutral range, and the distance between the data points and the laser ranging sensor is used to determine whether each group of data is the same vehicle; In the analog signal data, the number of neutrals between consecutive points is not greater than a certain value X, the data is directly grouped according to a certain range of neutral, and combined with the distance of the data points to the laser ranging sensor to determine whether each group of data is the same car. , where the neutral is the time when the distance measurement system does not detect the data.
  • the correlation coefficient of 1/10 and 1/10 of the front of a car is ⁇ 0.85, or does not exist, the following calculation is performed: starting from the first point data, simultaneously selecting two columns of data, the first group If the non-neutral data with a length greater than 45 exists at the same time, and the data of the two columns differs within 3 points, the Pearson correlation coefficient is calculated according to the length of the short array, and the speed is calculated by the coefficient >0.85. Not be considered.
  • the form is converted into a distance-related length (DL) form, that is, the true shape and size characteristic data on the side of the horizontal section of the vehicle is restored, and the vehicle type is identified by the number of vehicle axles and the length of the vehicle, and then to the side of the horizontal section of the vehicle.
  • the distance data of the laser distance measuring sensor is stored in the shape and size characteristic database on the horizontal section side of the vehicle as the target vehicle database (the vehicle speed detection can be performed according to this).
  • the target vehicle data is two-dimensional data, the horizontal axis is the length, and the vertical axis is the distance.
  • the time interval of the signal acquired by the dual sensor and the speed of the vehicle are multiplied to restore the true shape and size data of the horizontal section of the vehicle and fit, Then the distance from the horizontal section of the vehicle to the distance of the laser ranging sensor is obtained.
  • the graph in which the horizontal axis is time (in ms) and the vertical axis is distance (in mm) is converted into a graph in which the horizontal axis is the length (unit: mm) and the vertical axis is the distance (unit: mm).
  • Figure 5 is converted to Figure 6.
  • the data obtained in this way is the coordinates of the real two-dimensional plane, and does not scale with the speed.
  • the coordinates of the two-dimensional plane are the true shape of the horizontal section of the wheel, and the size data is related to the horizontal side of the wheel to the shoulder distance.
  • Data, and finally the data of the dual sensor transformation is fitted to obtain a more realistic two-dimensional plane data of the horizontal shape of the horizontal section of the wheel and the dimension data associated with the wheel horizontal section side to the shoulder distance data, as shown in FIG. .
  • the original wheel data is the actual shape and size data of the horizontal section side of the wheel which has the characteristic of “convex concave and convex”.
  • the original wheel data is two-dimensional data, the horizontal axis is the length, and the vertical axis is the distance. In the laboratory environment, the method of acquiring the target vehicle is adopted, and only the wheel is measured, which will not be described here.
  • the wheel track lateral distribution feature measurement system retrieves the similarity between the target vehicle database and the original wheel database from the shape and size feature database on the horizontal section side of the vehicle, and then selects the target wheel from the target vehicle database. And stored in the target wheel database, the target wheel data content includes the shape and size of one side of the horizontal section of the wheel and the distance from the horizontal section of the wheel to the laser.
  • the target wheel data is two-dimensional data, the horizontal axis is the length, and the vertical axis is the distance;
  • the specific screening method of the target wheel is: starting from the back end of any data in the target vehicle database, and performing Pearson correlation calculation one by one with each column data in the original wheel database. When the Pearson correlation coefficient is greater than 0.95, the Pearson correlation coefficient is greater than 0.95. A segment of the listed vehicle data is intercepted and stored in the target wheel data.
  • the detection may be greatly deviated from the standard data. Therefore, in order to improve the data recognition success rate, the original data should be blurred in a part of the area, and the similarity is appropriately reduced. standard.
  • the measuring system of the present invention is set on the side of the highway, about 1.5 meters away from the shoulder, laser
  • the distance measuring sensor is about 960mm from the ground.
  • the plane where the two lasers are located is parallel to the road surface.
  • the laser is perpendicular to the direction of travel of the vehicle.
  • a DV camera is placed on the right side of the measuring system and perpendicular to the laser at a distance of 9 meters.
  • the radar speed measuring device measures the vehicle speed at an angle of 15° with the driving direction of the vehicle; test the length of time measured in 25 minutes, and the DV camera is within 25 minutes. It has been in the recording state.
  • the radar speed measuring device measures the speed and records, and the laser vehicle detection system starts to record data.
  • the video is first reviewed, the vehicle is sequentially sorted and the time of the vehicle and the measured position is recorded; secondly, the vehicle speed recorded by the radar speed measuring device is read and associated with the vehicle type and time; and then the vehicle is identified according to the method of the present invention.
  • the vehicle data is obtained by vehicle identification and speed calculation in turn, and the number of axles is determined by the number of wheels having the characteristic curve of "convex concave and convex" on the side of the horizontal section of the wheel to identify the vehicle); finally, we will use the DV camera and the radar speed measuring device.
  • the obtained vehicle type and vehicle speed data are compared with the vehicle type and vehicle speed data obtained by the present invention to prove the accuracy, feasibility and advancement of the present invention.
  • the actual measurement map is the true shape and size characteristic data of the horizontal section side of the vehicle restored by the method of the present invention, and the number of axles is determined by the wheel having the characteristic curve of the “convex concave and convex” in the actual measurement map, and the vehicle type identification is performed.
  • the number of axles and vehicle data obtained by the system and method of the present invention are consistent with the number of axles and models obtained by the DV and the radar speed measuring device, and the ratio between the speed obtained by the radar speed measuring device is about It is 1:1.014, which shows that the invention can achieve speed detection and automatic identification of models.
  • the arrangement of the measuring system of the present invention is as in Experiment 1.
  • a high-resolution wide-angle camera is disposed about 5 m to the right of the measuring system of the present invention for photographing the last wheel of the right rear wheel when the vehicle is traveling in a slow lane.
  • the specific position that is pressed is the lateral distribution of the vehicle track, as well as the shape and size characteristics of the wheel.
  • the test was also carried out for a length of time measured in 25 minutes.
  • the high-resolution wide-angle camera was in the recording state for 25 minutes, and the measurement system of the present invention began recording data when a vehicle passed.
  • the measured map is a schematic diagram of the true dimension of the wheel horizontal section on the side of the fitted wheel of each group of vehicles measured by the present invention, and the data is the tire fitting data.
  • the present invention can calculate the law of the lateral distribution coefficient of the wheel track under different conditions, and can investigate the influence of factors such as different time periods, vehicle speed, vehicle density, lane width and vehicle composition on the distribution coefficient;

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  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • General Physics & Mathematics (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Length Measuring Devices By Optical Means (AREA)
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Abstract

一种轮迹横向分布测量系统（1），包括车辆水平剖面一侧到路肩的距离测量系统、车辆水平剖面一侧的形状和尺寸特征数据库、轮迹横向分布特征测定系统和轮迹横向分布特征数据库，利用轮迹横向分布测量系统（1）进行测量的方法包括以下步骤：车辆水平剖面一侧到路肩的距离测量系统测定车辆水平剖面一侧的形状、尺寸数据和车辆水平剖面一侧到激光测距传感器的距离数据；车辆识别；速度计算；车型识别；建原始车轮数据库；车轮识别；制作轮迹横向分布特征曲线。可以统计出不同情况下轮迹横向分布系数的规律，测量精度高，并且可以实现全天候数据收集。

Description

轮迹横向分布测量系统及测量方法 技术领域
本发明属于交通运输工程及公路路基路面技术领域，特别涉及一种轮迹横向分布测量系统及测量方法。
背景技术
载重型货车车轮经常驶过的地方，路面由于长期受到荷载，荷载由路面传递到路基上，轻微者表现为路面少许凹陷留下车辙，甚者则表现为路基被严重破坏，路面塌陷以及形成裂缝、边缘凸起。尤其在路基路面设计强度比较低而通过车辆多为载重型货车的道路上，这些迹象更为明显。这种路基路面被严重损毁的现象背后的原因是大部分载重型货车车轮高频率地经过道路横断面上的某些位置，而该横断面上的其他位置几乎未被使用。
在美国国家沥青路面实验跑道(NCAT)，轮胎路径之间的中心位置被用作车辙深度测量的参考点，因为轮胎路径以外的道路几乎没有受到破坏。这也表明，即使路面已经损坏到无法正常使用，但仍有部分轮胎路径以外的路面保持完好。所以，想要研究路基路面被载重型货车损毁的特征就必须要研究载重型货车行驶时轮胎在道路横断面上的分布特征，即轮迹横向分布特征。轮迹横向分布：是指车辆在道路上行驶时候，车轮的轮迹总是在横断面中心线附近一定给范围内左右摇摆，并按一定的频率分布在车道横断面上。不同地区轮迹横向分布的特征各有差异，这就突出了路面结构不均匀设计的重要性，而我们研究轮迹横向分布的意义就在于，轮迹横向分布系数可以客观地描述车辆轮迹横向位置的分布特点，对于精确地进行路面结构力学分析具有重要的意义。
而现行测量轮迹横向分布特征的主要方法是，现场交通摄影录像法，这种方法需要交通安全部门和道路管理部门配合4-5个测量人员封路之后在路面上画某种标尺，解封道路之后再通过摄像机拍摄车轮驶过时压过标尺的位置，路面拍摄结束后测量人员通过观看视频录像去记录数据，其显著缺点有：需要大量的人工和大量的时间，一般都是阶段性、短暂性的观测，路面标线的设置、摄影设备的摆放位置以及调查人员的出现不仅会干扰司机的正常驾驶以至于影响测量数据的客观性和精确度，还会对测量人员的人身安全以及整条道路系统的正常运行和交通安全带来极大的隐患，再加上地标线的设置不规范、不合理可能导致的测量结果精确度低，总之是费时费力效率效益低，危险系数高，操作难度大，可行性不强，无法实现全天候无人值守自动识别车辆与自动分车型测速。
本发明提供一种全新的、可自动识别车辆与自动分车型测速的、自动化的、全天候无人值守的轮迹横向分布测量系统和方法，不仅为车型和车轮识别、分车型车速检测等创造了条件；适应自动化的需求，以及科研和社会生产建设对效率、效益、精确度和安全性的要求。纵观世界上的轮迹横向分布特征研究数据，国外有一些学者对轮迹横向分布进行了实测，得到了适用于当地情况的典型值，而我国的轮迹横向分布系数仍旧停留在全国的统计平均值这一水平，本项目的发明以及实验有利于地方机构进行轮迹横向分布观测，尤其是分车型进行轮迹横向分布观测和获取地方性的轮迹横向分布数据，能显著加强地区性的路基路面结构设计精度。
发明内容
针对现有技术存在的不足，本发明提供一种全新的、自动化的轮迹横向分布测量系统及测量方法，实现车型和车轮分类自动检测、交通调查、分车型车速检测以及公路轮迹横向分布观测。相比较传统现场交通摄影录像法，本系统的测量精度有了极大的提升，效率高，无人值守，并且可以实现全天候数据收集。
为了解决上述技术问题，本发明采用的技术方案是：一种轮迹横向分布测量系统，包括车辆水平剖面一侧到路肩的距离测量系统、车辆水平剖面一侧的形状和尺寸特征数据库、轮迹横向分布特征测定系统和轮迹横向分布特征数据库。
所述车辆水平剖面一侧到路肩的距离测量系统测定车辆水平剖面一侧的形状、尺寸特征数据和车辆水平剖面一侧到路肩的距离数据，并将数据保存到车辆水平剖面一侧形状、尺寸特征数据库，
所述车辆水平剖面一侧形状、尺寸特征数据库，用于存储实测的样本车辆水平剖面一侧的形状、尺寸特征数据、车辆水平剖面一侧到路肩的距离数据和本系统原始输入的车轮水平剖面一侧的形状、尺寸特征数据；
所述轮迹横向分布特征测定系统分析样本车辆水平剖面一侧的形状、尺寸特征数据和样本车辆水平剖面一侧到路肩的距离数据并最终测定轮迹横向分布特征；
所述轮迹横向分布特征数据库，用于存储轮迹横向分布特征数据，并可据此模拟建立轮迹横向分布特征频率模型、确定和输出轮迹横向分布系数，进而研究车辆荷载对路面的长期损毁影响。
优选的，所述车辆水平剖面一侧到路肩的距离测量系统包括一块嵌入式开发板和两台超高频的激光测距传感器，所述两台超高频的激光测距传感器沿行车方向在路边同一高度的水平线上摆放，同时工作，将数据保存到车辆水平剖面一侧的形状和尺寸特征数据库。
优选的，所述车辆水平剖面一侧到路肩的距离测量系统对数据初步筛选、数据加工处理和输出；初步筛选是指，只选取一定目标内的有效样本数据；数据加工处理是指，通过计算车辆速度V，将两台超高频的激光测距传感器采集到的原始数据拟合处理，并同时将数据由距离关联时间(D-T)的形式转化为距离关联长度(D-L)的形式，即还原出车辆水平剖面一侧的真实形状、尺寸特征数据，并输出到车辆水平剖面一侧的形状和尺寸特征数据库。
优选的，所述车辆水平剖面一侧的形状和尺寸特征数据库包括A区、B区和C区三大部分，A区用于存储车辆水平剖面一侧到路肩的距离测量系统所测的车辆水平剖面一侧的形状、尺寸特征和车辆水平剖面一侧到路肩的距离的有效样本数据，即目标车辆数据库；B区用于存储数据库原始输入的车轮水平剖面一侧形状、尺寸特征数据，即原始车轮数据库；C区用于存储从A区筛选出的样本车轮水平剖面一侧形状、尺寸特征数据，即目标车轮数据库；B区的数据可持续添加C区的数据进行自动更新；A区和B区的数据可供轮迹横向分布特征测定系统调用。
轮迹横向分布测量方法，包括以下步骤：
1)车辆水平剖面一侧到路肩的距离测量系统测定车辆水平剖面一侧的形状、尺寸数据和车辆水平剖面一侧到激光测距传感器的距离数据，所述车辆水平剖面是指由激光测距传感器架设高度所在的、与地面平行的平面剖切车辆所获得的剖面，所述车辆水平剖面一侧到路肩的距离测量系统包括两台沿行车方向在路边同一高度的水平线上摆放的超高频的激光测距传感器；
2)车辆识别：识别出完整的车辆数据，所述完整的车辆数据是指某样本车辆单独经过车辆水平剖面一侧到路肩的距离测量系统时，距离测量系统获得的所有的电压模拟信号数据；
3)速度计算：计算出车辆的真实速度,再加入双传感器获取信号的时间间隔，将电压模拟信号数据转化为长度数据，还原出车辆水平剖面一侧的真实形状和尺寸特征数据，并通过车辆轴数和车辆长度进行车型识别，然后和车辆水平剖面一侧到激光测距传感器的距离数据一同存入车辆水平剖面一侧的形状和尺寸特征数据库，作为目标车辆数据库；
4)建原始车轮数据库：在车辆水平剖面一侧的形状和尺寸特征数据库中输入原始车轮数据，所述原始车轮数据是在视觉上具有“凸凹凸凹凸”特征的车轮水平剖面一侧实际形状、尺寸数据；
5)车轮识别：轮迹横向分布特征测定系统从车辆水平剖面一侧的形状和尺寸特征数据库中调取目标车辆数据库与原始车轮数据库进行相似度对比，进而从目标车辆数据库中筛选出 目标车轮，并存入目标车轮数据库，目标车轮数据内容包括车轮水平剖面一侧的形状、尺寸和车轮水平剖面一侧到激光器的距离；
6)利用目标车轮数据库的数据取点制作轮迹横向分布特征曲线、轮迹横向分布系数，也可利用目标车轮数据库的数据获得所测样本车辆的轮胎、轮辋、轮辐和轮毂尺寸数据，并将数据存入轮迹横向分布特征数据库。
优选的，步骤2)的车辆识别的方法是：判断电压模拟信号数据中连续点之间空挡的个数是否大于某个值进行目标车辆初筛并将数据分成若干段，再对每段数据依据某个空挡范围进行分组，再结合数据点到激光测距传感器的距离判断每组数据是否为同一辆车。
优选的，车辆识别完成后，对同一车辆经过时双传感器获得的两列数据进行皮尔森相关计算，相关系数大于0.85时认为可计算速度；步骤3)的速度计算方法是：同一个车轮的水平剖面一侧需要经过两个超高频激光测距传感器扫描，再利用两个传感器之间的空间水平距离S和各自获得首个数据的时间差△t计算出车辆的行驶速度V，进而将数据由距离关联时间(D-T)的形式转化为距离关联长度(D-L)的形式，即还原出车辆水平剖面一侧的真实形状和尺寸特征数据，并输出到车辆水平剖面一侧形状、尺寸特征数据库。
优选的，目标车辆数据是二维数据，横轴为长度，纵轴为距离，由双传感器获取信号的时间间隔和车辆的速度相乘还原出车辆水平剖面一侧的真实形状、尺寸数据并进行拟合，再关联车辆水平剖面一侧到激光测距传感器的距离而得。
优选的，原始车轮数据是二维数据，横轴为长度，纵轴为距离，是在实验室环境下，采用同获取目标车辆的方法，仅测量车轮而得。
优选的，步骤5)的目标车轮的筛选方法是，从目标车辆数据库中的任一条数据的后端依次往前，与原始车轮数据库中的每一条数据逐个进行皮尔森相关计算，皮尔森相关系数大于0.95时，将该列车辆数据的某一段截取并存入目标车轮数据；目标车轮数据是二维数据，横轴为长度，纵轴为距离。
与现有技术相比，本发明优点在于：
(1)本发明可以统计出不同情况下轮迹横向分布系数的规律，可考察不同时间段、车速、车辆密度、车道宽、车辆组成等因素对分布系数的影响。
(2)相比较传统现场交通摄影录像法，本系统的测量精度有了极大的提升，并且可以实现全天候、无人值守、自动收集数据。
(3)本发明有两台高精度、超高频的激光测距传感器同工作，并将所测数据进行拟合，它能保证和极大地提高轮迹横向分布观测的效率和精度，并可以对观测数据进行初选处理并储 存，储存的数据包括轮迹横向分布数据以及车轮水平剖面一侧的形状、尺寸特征数据。在要求与传统公路设计寿命相同的情况下，通过分析车辆轮迹横向分布的区域性特征，进行精确的路基路面结构设计，缓解轮迹沉降和裂缝的发展，减少材料消耗，降低道路养护频率；结合轮迹横向分布特征更精准地分析车辆荷载对路面结构的损毁影响，从而加强某一路段或某一条车道的路基路面结构设计，减少不必要的资源浪费，增长道路使用年限。
附图说明
图1为本发明的轮迹横向分布测量系统的布局主视图；
图2为图1的左视图；
图3为车轮水平剖切位置示意图；
图4为激光在车轮水平剖面一侧扫过时车轮“凸凹凸凹凸”特征曲线示意图；
图5为双传感器扫描时车轮水平剖面一侧形状示意图(时间/距离)；
图6为数据还原车轮水平一侧的真实形状、尺寸示意图(长度/距离)；
图7为拟合后车轮水平剖面一侧真实尺寸关联车轮水平剖面一侧到路肩距离示意图(长度/距离)；
图8为本发明的轮迹横向分布测量方法流程图；
图中，1.轮迹横向分布测量系统；2.车轮；3.车轴；4.车轮水平剖切位置；5.激光光线；6.凸处；7.凹处；8.轮毂空洞。
具体实施方式
下面结合附图及具体实施例对本发明作进一步的说明。
本发明以六轴货车为例进行说明。本发明的轮迹横向分布测量系统，包括车辆水平剖面一侧到路肩的距离测量系统、车辆水平剖面一侧的形状和尺寸特征数据库、轮迹横向分布特征测定系统和轮迹横向分布特征数据库。
车辆水平剖面一侧到路肩的距离测量系统测定车辆水平剖面一侧的形状、尺寸特征数据和车辆水平剖面一侧到路肩的距离数据，并将数据保存到车辆水平剖面一侧形状、尺寸特征数据库；
车辆水平剖面一侧形状、尺寸特征数据库，用于存储实测的样本车辆水平剖面一侧的形状、尺寸特征数据、车辆水平剖面一侧到路肩的距离数据和本系统原始输入的车轮水平剖面一侧的形状、尺寸特征数据；
轮迹横向分布特征测定系统分析样本车辆水平剖面一侧的形状、尺寸特征数据和样本车辆水平剖面一侧到路肩的距离数据并最终测定轮迹横向分布特征；
轮迹横向分布特征数据库，用于存储轮迹横向分布特征数据，并可据此模拟建立轮迹横向分布特征频率模型、确定和输出轮迹横向分布系数，进而研究车辆荷载对路面的长期损毁影响。
车辆水平剖面一侧到路肩的距离测量系统包括一块嵌入式开发板和两台超高频的激光测距传感器，还具有独立电源和存储器，具有数据输入、输出和存储功能。如图1-3所示，六轴货车有六组车轮2和车轴3，两台超高频的激光测距传感器沿行车方向在路边同一高度的水平线上摆放，激光光线5垂直于汽车行驶方向，激光发射点距离路面竖直高度(H)为货车车轮轮毂凸起处的共同高度，货车车轮种类繁多，故H有一定的取值范围，车轮水平剖切位置4是指由激光测距传感器架设高度所在的、与地面平行的平面剖切车辆所获得的剖面，激光发射点位于道路横断面一侧的路肩位置，两激光测距传感器同时工作，将数据保存到车辆水平剖面一侧的形状和尺寸特征数据库。
由于小客车的各轴及诸多货车的前轴对路面的破坏作用较小，所以本发明只需区分载重型货车双轮承重轴与其它类型车轴即可。载重型货车承重轴需要承载较大的重量，所以一般采用单侧双轮组的构造。如附图4所示，车轮水平剖面由于车轴3的存在，一侧的轮廓线具有凸处6和凹处7，轮廓线特征表现为“凸凹凸凹凸”的形状。载重型货车承重轴的双轮组由于安装需要，内侧车轮凹面向里，外侧车轮凹面向外，这样才可以让外侧轮胎也与车轴连在一起。
当车辆经过时，可以检测到高度H处车辆车头到车尾一侧的全部数据，此时的数据形式为时间关联距离的电压模拟信号，距离是车辆水平剖面一侧到激光测距传感器的距离，将数据保存。
车辆水平剖面一侧到路肩的距离测量系统对数据初步筛选、数据加工处理和输出；初步筛选是指，只选取一定目标内的有效样本数据；数据加工处理是指，通过计算车辆速度V，将两台超高频的激光测距传感器采集到的原始数据拟合处理，并同时将数据由距离关联时间(D-T)的形式转化为距离关联长度(D-L)的形式，即还原出车辆水平剖面一侧的真实形状、尺寸特征数据，并输出到车辆水平剖面一侧的形状和尺寸特征数据库。图5为双传感器扫描时车轮水平剖面一侧形状示意图，数据为距离关联时间(D-T)的形式；图6为数据还原车轮水平一侧的真实形状、尺寸示意图，数据为距离关联长度(D-L)的形式。图7为拟合后车轮水平剖面一侧真实尺寸关联车轮水平剖面一侧到路肩距离示意图，数据为距离关联长度(D-L)的形式。
车辆水平剖面一侧的形状和尺寸特征数据库包括A区、B区和C区三大部分，A区用于存储车辆水平剖面一侧到路肩的距离测量系统所测的车辆水平剖面一侧的形状、尺寸特征和车辆水平剖面一侧到路肩的距离的有效样本数据，即目标车辆数据库；B区用于存储数据库原始输入的车轮水平剖面一侧形状、尺寸特征数据，即原始车轮数据库；C区用于存储从A区筛选出的样本车轮水平剖面一侧形状、尺寸特征数据，即目标车轮数据库；B区的数据可持续添加C区的数据进行自动更新；A区和B区的数据可供轮迹横向分布特征测定系统调用。
如图8所示，轮迹横向分布测量方法，包括以下步骤：
1)车辆水平剖面一侧到路肩的距离测量系统测定车辆水平剖面一侧的形状、尺寸数据和车辆水平剖面一侧到激光器的距离数据；
2)车辆识别：识别出完整的车辆数据，即从数据中识别出完整的电压模拟信号数据，具体方法如下：将双传感器获得的数据同时处理，若电压模拟信号数据中连续点之间空挡的个数大于某个值X则将数据分成若干段，再对每段数据依据某个空挡范围进行分组，并结合数据点到激光测距传感器的距离判断每组数据是否为同一辆车；若电压模拟信号数据中连续点之间空挡的个数不大于某个值X则将数据直接依据某个空挡范围进行分组，并结合数据点到激光测距传感器的距离判断每组数据是否为同一辆车，其中的空挡是指距离测量系统没有检测到数据的时间。
3)速度计算：车辆识别完成后，判断为同一车辆，对同一车辆经过时双传感器获得的两列数据进行皮尔森相关计算，相关系数大于0.85时认为可计算速度，具体方法如下：同时选取双传感器获得的两列完整的车辆电压模拟信号数据的前1/10和后1/10进行数据数量对比,不等时，以较长的为标准，数据一致，进行皮尔森相关系数计算；对于同一辆车的数据，前1/10和后1/10各存在一个相关系数，若前1/10或后1/10存在一个系数大于0.85，则认为该车两列数据较一致，可进行速度计算；若某辆车的车前1/10和后1/10的相关系数都<0.85,或者不存在，则进行以下计算：从第一个点数据开始，同时选择两列数据中，第一组长度大于45的非空挡数据，如果同时存在，且两列的该组数据相差在3点以内，以短数组长度为标准，进行皮尔森相关系数计算，系数>0.85的进行速度计算，否则不予考虑。
速度计算方法是：利用两个传感器之间的空间水平距离S和各自获得首个数据的时间差△t计算出车辆的行驶速度V，V＝S/△t，进而将数据由距离关联时间(D-T)的形式转化为距离关联长度(D-L)的形式，即还原出车辆水平剖面一侧的真实形状和尺寸特征数据，并通过车辆轴数和车辆长度进行车型识别，然后和车辆水平剖面一侧到激光测距传感器的距离数据 一同存入车辆水平剖面一侧的形状和尺寸特征数据库，作为目标车辆数据库(可据此进行分车型车速检测。)
目标车辆数据是二维数据，横轴为长度，纵轴为距离，由双传感器获取信号的时间间隔和车辆的速度相乘还原出车辆水平剖面一侧的真实形状、尺寸数据并进行拟合，再关联车辆水平剖面一侧到激光测距传感器的距离而得。
经过变换后，由横轴是时间(单位ms)、纵轴为距离(单位mm)的图表，转化为横轴为长度(单位mm)、纵轴为距离(单位mm)的图表，即由附图5转化为附图6。这样以来得到的数据为真实的二维平面的坐标，不会随速度的快慢产生缩放，二维平面的坐标即为车轮水平剖面一侧的真实形状、尺寸数据关联车轮水平一侧到路肩距离的数据，最后再将双传感器转化后的数据进行拟合得到更为真实的车轮水平剖面一侧形状、尺寸数据关联车轮水平剖面一侧到路肩距离数据的二维平面数据，如附图7所示。
4)建原始车轮数据库：在车辆水平剖面一侧的形状和尺寸特征数据库中输入原始车轮数据，原始车轮数据是在视觉上具有“凸凹凸凹凸”特征的车轮水平剖面一侧实际形状、尺寸数据；原始车轮数据是二维数据，横轴为长度，纵轴为距离，是在实验室环境下，采用同获取目标车辆的方法，仅测量车轮而得，此处不再赘述。
5)车轮识别：轮迹横向分布特征测定系统从车辆水平剖面一侧的形状和尺寸特征数据库中调取目标车辆数据库与原始车轮数据库进行相似度对比，进而从目标车辆数据库中筛选出目标车轮，并存入目标车轮数据库，目标车轮数据内容包括车轮水平剖面一侧的形状、尺寸和车轮水平剖面一侧到激光器的距离，目标车轮数据是二维数据，横轴为长度，纵轴为距离；
目标车轮的具体筛选方法是：从目标车辆数据库中的任一条数据的后端依次往前，与原始车轮数据库中的每一列数据逐个进行皮尔森相关计算，皮尔森相关系数大于0.95时，将该列车辆数据的某一段截取并存入目标车轮数据。
6)利用目标车轮数据库的数据取点制作轮迹横向分布特征曲线、轮迹横向分布系数，也可利用目标车轮数据库的数据获得所测样本车辆的轮胎、轮辋、轮辐和轮毂尺寸数据，并将数据存入轮迹横向分布特征数据库。
如图3，由于货车轮毂上分布有轮毂空洞8，检测时可能会与标准数据有较大偏差，所以为了提高数据识别成功率，要对原始数据在部分区域做模糊处理，并适当降低相似度标准。
下面通过两组实验对比，证明本发明用于车辆轮迹横向分布测定的可行性和精确性。
实验一
在G15沈海高速广州方向3456段进行检测，为保证测量数据的客观性，不影响车辆正常行驶和交通安全，本发明的测量系统设置在高速公路一侧、距离路肩约1.5米左右处，激光测距传感器距离地面高度约960mm，两束激光所在的平面与路面平行，激光与车辆行驶方向垂直；在测量系统的右侧、与激光垂直直线距离为9米处设置一台DV摄像机，用于记录驶过车辆的车型；紧邻DV摄像机的地方设置一部雷达测速装置，雷达测速装置与车辆行驶方向呈15°角测量车速；以25分钟为测量的时间长度进行试验，DV摄像机在25分钟内一直处于录像状态，当有车辆经过时，雷达测速装置测速并记录，同时激光车辆检测系统开始记录数据。
测量试验完成后，首先回看录像，给车辆依次排序并记录车型和经过测量位置的时间点；其次读取雷达测速装置记录的车速，并与车型和时间关联；然后依据本发明的方法识别车辆(依次经过车辆识别、速度计算获得目标车辆数据，并通过具有车轮水平剖面一侧具有“凸凹凸凹凸”特征曲线的车轮数量判断车轴数量，去识别车辆)；最后我们将DV摄像机和雷达测速装置获得的车型、车速数据与利用本发明获得的车型、车速数据进行对比，以证明本发明的精确性、可行性和先进性。
以任一次25分钟的试验内经过的载重型货车为样本数据，具体数据和对比如表1所示。
表1
Figure PCTCN2018072785-appb-000001
Figure PCTCN2018072785-appb-000002
其中，该实测图为利用本发明的方法还原的车辆水平剖面一侧的真实形状和尺寸特征数据，通过实测图中具有“凸凹凸凹凸”特征曲线的车轮判断车轴的数量，进行车型识别。
表1可知，经检验对比，利用本发明的系统和方法获得的轴数和车型数据均与DV和雷达测速装置获得的轴数和车型一致，与雷达测速装置获得的车速之间的比例均约为1:1.014，说明本发明可以做到分车型速度检测和车型自动识别。
实验二
本发明的测量系统的布置方式如实验一，在本发明的测量系统的正右方约5m处设置一个高分辨率广角摄像机，用于拍摄车辆在慢车道行驶时，右后方的最后一个车轮碾压过的具体位置即车辆轮迹横向分布，以及车轮的形状和尺寸特征。同样以25分钟为测量的时间长度进行试验，高分辨率广角摄像机在25分钟内一直处于录像状态，当有车辆经过时，本发明的测量系统开始记录数据。
测量试验完成后，首先回看录像，记录车辆的轮迹横向分布数据、车轮的形状和尺寸特征以及经过测量位置的时间点，然后利用本发明的方法获得目标车轮的形状和尺寸特征，并取点制作车辆的轮迹横向分布特征。
以任一次25分钟的试验内经过的载重型货车为样本数据，具体数据和对比如表2所示。
表2
Figure PCTCN2018072785-appb-000003
其中，该实测图为利用本发明测得的各组车辆的拟合后车轮水平剖面一侧真实尺寸关联车轮水平剖面一侧到路肩距离示意图，数据为轮胎拟合数据。
由表2可知，经检验对比，利用本发明获得的车轮类型和车轮尺寸数据均与高分辨率广角摄像机获得的车轮类型和车轮尺寸数据一致，此外，令数字信号的值为X，由公式Y＝2.3811X-4579.7计算得出车辆的轮迹横向分布特征值，与高分辨率广角摄像机获得的轮迹横向分布 数据的方差为R 2＝0.97，考虑看视频回放记录轮迹横向分布数据时的人为因素以及路面标尺设置时可能为产生的误差，本发明获得车辆轮迹横向分布的可行性、精确性是极好的。
综上所述，本发明可以统计出不同情况下轮迹横向分布系数的规律，可考察不同时间段、车速、车辆密度、车道宽、车辆组成等因素对分布系数的影响；通过分析车辆轮迹横向分布的区域性特征，进行精确的路基路面结构设计，结合轮迹横向分布特征更精准地分析车辆荷载对路面结构的损毁影响；相比较传统现场交通摄影录像法，本系统的测量精度有了极大的提升，并且可以实现全天候数据收集。
当然，上述说明并非是对本发明的限制，本发明也并不限于上述举例，本技术领域的普通技术人员，在本发明的实质范围内，作出的变化、改型、添加或替换，都应属于本发明的保护范围。

Claims (10)

  1. 一种轮迹横向分布测量系统，包括车辆水平剖面一侧到路肩的距离测量系统、车辆水平剖面一侧的形状和尺寸特征数据库、轮迹横向分布特征测定系统和轮迹横向分布特征数据库，其特征在于：
    所述车辆水平剖面一侧到路肩的距离测量系统测定车辆水平剖面一侧的形状、尺寸特征数据和车辆水平剖面一侧到路肩的距离数据，并将数据保存到车辆水平剖面一侧形状、尺寸特征数据库，
    所述车辆水平剖面一侧形状、尺寸特征数据库，用于存储实测的样本车辆水平剖面一侧的形状、尺寸特征数据、车辆水平剖面一侧到路肩的距离数据和本系统原始输入的车轮水平剖面一侧的形状、尺寸特征数据；
    所述轮迹横向分布特征测定系统分析样本车辆水平剖面一侧的形状、尺寸特征数据和样本车辆水平剖面一侧到路肩的距离数据并最终测定轮迹横向分布特征；
    所述轮迹横向分布特征数据库，用于存储轮迹横向分布特征数据，并可据此模拟建立轮迹横向分布特征频率模型、确定和输出轮迹横向分布系数，进而研究车辆荷载对路面的长期损毁影响。
  2. 根据权利要求1所述的轮迹横向分布测量系统，其特征在于：所述车辆水平剖面一侧到路肩的距离测量系统包括一块嵌入式开发板和两台超高频的激光测距传感器，所述两台超高频的激光测距传感器沿行车方向在路边同一高度的水平线上摆放，同时工作，将数据保存到车辆水平剖面一侧的形状和尺寸特征数据库。
  3. 根据权利要求2所述的轮迹横向分布测量系统，其特征在于：所述车辆水平剖面一侧到路肩的距离测量系统对数据初步筛选、数据加工处理和输出；初步筛选是指，只选取一定目标内的有效样本数据；数据加工处理是指，通过计算车辆速度V，将两台超高频的激光测距传感器采集到的原始数据拟合处理，并同时将数据由距离关联时间(D-T)的形式转化为距离关联长度(D-L)的形式，即还原出车辆水平剖面一侧的真实形状、尺寸特征数据，并输出到车辆水平剖面一侧的形状和尺寸特征数据库。
  4. 根据权利要求3所述的轮迹横向分布测量系统，其特征在于：所述车辆水平剖面一侧的形状和尺寸特征数据库包括A区、B区和C区三大部分，A区用于存储车辆水平剖面一侧到路肩的距离测量系统所测的车辆水平剖面一侧的形状、尺寸特征和车辆水平剖面一侧到路肩的距离的有效样本数据，即目标车辆数据库； B区用于存储数据库原始输入的车轮水平剖面一侧形状、尺寸特征数据，即原始车轮数据库；C区用于存储从A区筛选出的样本车轮水平剖面一侧形状、尺寸特征数据，即目标车轮数据库；B区的数据可持续添加C区的数据进行自动更新；A区和B区的数据可供轮迹横向分布特征测定系统调用。
  5. 利用权力要求1所述的测量系统进行的轮迹横向分布测量方法，其特征在于：包括以下步骤，
    1)车辆水平剖面一侧到路肩的距离测量系统测定车辆水平剖面一侧的形状、尺寸数据和车辆水平剖面一侧到激光测距传感器的距离数据，所述车辆水平剖面是指由激光测距传感器架设高度所在的、与地面平行的平面剖切车辆所获得的剖面，所述车辆水平剖面一侧到路肩的距离测量系统包括两台沿行车方向在路边同一高度的水平线上摆放的超高频的激光测距传感器；
    2)车辆识别：识别出完整的车辆数据，所述完整的车辆数据是指某样本车辆单独经过车辆水平剖面一侧到路肩的距离测量系统时，距离测量系统获得的所有的电压模拟信号数据；
    3)速度计算：计算出车辆的真实速度,再加入双传感器获取信号的时间间隔，将电压模拟信号数据转化为长度数据，还原出车辆水平剖面一侧的真实形状和尺寸特征数据，并通过车辆轴数和车辆长度进行车型识别，然后和车辆水平剖面一侧到激光测距传感器的距离数据一同存入车辆水平剖面一侧的形状和尺寸特征数据库，作为目标车辆数据库；
    4)建原始车轮数据库：在车辆水平剖面一侧的形状和尺寸特征数据库中输入原始车轮数据，所述原始车轮数据是在视觉上具有“凸凹凸凹凸”特征的车轮水平剖面一侧实际形状、尺寸数据；
    5)车轮识别：轮迹横向分布特征测定系统从车辆水平剖面一侧的形状和尺寸特征数据库中调取目标车辆数据库与原始车轮数据库进行相似度对比，进而从目标车辆数据库中筛选出目标车轮，并存入目标车轮数据库，目标车轮数据内容包括车轮水平剖面一侧的形状、尺寸和车轮水平剖面一侧到激光器的距离；
    6)利用目标车轮数据库的数据取点制作轮迹横向分布特征曲线、轮迹横向分布系数，也可利用目标车轮数据库的数据获得所测样本车辆的轮胎、轮辋、轮辐和轮毂尺寸数据，并将数据存入轮迹横向分布特征数据库。
  6. 根据权利要求5所述的轮迹横向分布测量方法，其特征在于：步骤2)的车辆识别的方法是：判断电压模拟信号数据中连续点之间空挡的个数是否大于某个值进行目标车辆初筛并将数据分成若干段，再对每段数据依据某个空挡范围进行分组，再结合数据点到激光测距传感器的距离判断每组数据是否为同一辆车。
  7. 根据权利要求5所述的轮迹横向分布测量方法，其特征在于：车辆识别完成后，对同一车辆经过时双传感器获得的两列数据进行皮尔森相关计算，相关系数大于0.85时认为可计算速度；步骤3)的速度计算方法是：同一个车轮的水平剖面一侧需要经过两个超高频激光测距传感器扫描，再利用两个传感器之间的空间水平距离S和各自获得首个数据的时间差△t计算出车辆的行驶速度V，进而将数据由距离关联时间(D-T)的形式转化为距离关联长度(D-L)的形式，即还原出车辆水平剖面一侧的真实形状和尺寸特征数据，并输出到车辆水平剖面一侧形状、尺寸特征数据库。
  8. 根据权利要求5所述的轮迹横向分布测量方法，其特征在于：目标车辆数据是二维数据，横轴为长度，纵轴为距离，由双传感器获取信号的时间间隔和车辆的速度相乘还原出车辆水平剖面一侧的真实形状、尺寸数据并进行拟合，再关联车辆水平剖面一侧到激光测距传感器的距离而得。
  9. 根据权利要求5所述的轮迹横向分布测量方法，其特征在于：原始车轮数据是二维数据，横轴为长度，纵轴为距离，是在实验室环境下，采用同获取目标车辆的方法，仅测量车轮而得。
  10. 根据权利要求5所述的轮迹横向分布测量方法，其特征在于：步骤5)的目标车轮的筛选方法是，从目标车辆数据库中的任一条数据的后端依次往前，与原始车轮数据库中的每一条数据逐个进行皮尔森相关计算，皮尔森相关系数大于0.95时，将该列车辆数据的某一段截取并存入目标车轮数据；目标车轮数据是二维数据，横轴为长度，纵轴为距离。
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