Method for investigating road section by using automobile big data

Abstract

The invention discloses a method for investigating a road section by using automobile big data, and the method comprises the following steps: carrying out the statistics of the latitude and longitude coordinates of a plurality of vehicles in the same driving direction in a to-be-investigated map frame by using the latitude and longitude coordinate information in the automobile big data; a two-dimensional matrix with the same size as the to-be-investigated map framing is developed in a computer memory; for a road in any direction of east, south, west and north, making a horizontal line by taking 2n as a grid interval for the framing basic grids of the to-be-investigated map; traversing all the grids from left to right along the horizontal line, regarding the first longitude and latitude coordinate point as a left boundary point of the section until the first longitude and latitude coordinate point encounters a blank grid as a right boundary point of the section, and obtaining a road section slice. On the basis of the method, continuous investigation can be carried out on billions of road sections.

Description

Technical Field

[0001] The present invention relates to the technical field of road imaging, and in particular to a method for conducting road section survey using automobile big data. Background Art

[0002] Traffic flow survey is an important part of traffic engineering. Its survey results are widely used to determine and analyze the distribution pattern of vehicle speed and speed change trend at a certain location, important parameters of intersection traffic design, traffic accidents, judging the effectiveness of traffic improvement measures, road speed limits, basis for setting traffic signs, basis for traffic improvement design at local locations (such as curves, slopes, bottlenecks, etc. of the road), and important parameters for traffic flow theory research.

[0003] Conventional survey methods take the selected roads as the object, and set up measuring instruments on the selected road monitoring sections to monitor parameters such as vehicle flow, vehicle speed, and vehicle type. Conventional survey methods usually require manual on-site continuous monitoring for more than two days, and then obtain the cross-section survey results after internal data collation. Conventional survey methods have at least the following problems and shortcomings: (1) Conventional survey methods cannot complete the survey of billions of road sections in my country's current road network; (2) Conventional survey methods cannot conduct unlimited continuous surveys of road sections; (3) Conventional survey methods cannot obtain the internal operating parameters of the vehicle (such as torque, speed, etc.); (4) Conventional survey results are mainly based on the statistical results of vehicle distribution and traffic flow at a certain location, which are difficult to use as training data for AI models. Summary of the invention

[0004] In view of one or more of the problems existing in the prior art, the present invention provides a method for road section survey using automobile big data, comprising the following steps: S1. Taking the latitude and longitude of the initial frame data of a single vehicle as the origin, constructing the initial frame rectangular coordinate system, dividing the initial frame rectangular coordinate system into four coordinate azimuth intervals of north, east, south and west; S2. Determine the direction of travel of the vehicle on the road according to the position where the latitude and longitude of a single vehicle in subsequent frame data falls within the coordinate azimuth interval; S3. Using the longitude and latitude coordinate information in the automobile big data, statistics are collected on the longitude and latitude coordinates of multiple vehicles in the same driving direction in the map slice to be investigated; S4. Create a two-dimensional matrix in the computer memory with the same size as the map tiles to be investigated; S5. For roads in any direction of east, south, west or north, draw a horizontal line with a grid interval of 2^n on the basic grid of the survey map; S6. Traverse all grids from left to right along the horizontal line, and regard the first longitude and latitude coordinate point as the left boundary point of the section, until a blank grid is encountered as the right boundary point of the section, and obtain the road section slice.

[0005] Preferably, in step S5, n=4.

[0006] According to one aspect of the present invention, the generation of the coordinate azimuth comprises the following steps: SA1. In the above coordinate azimuth interval, the north direction is between 315° and 45°, the east direction is between 45° and 135°, the south direction is between 135° and 225°, and the west direction is between 225° and 315°.

[0007] According to one aspect of the present invention, the step S5 further includes the following steps: SF1. The vehicle operating condition indicators in the vehicle message are regarded as a sampling survey of the road section.

[0008] According to one aspect of the present invention, the step SF1 further includes the following steps: SG1. Express the individual indicators of the vehicle operating conditions on the road section as cumulative quantiles.

[0009] According to one aspect of the present invention, the single indicator in step SG1 is the vehicle speed, and the vehicle speed in the percentile speed interval of A%-B% on the road section is used as the safe driving speed interval of the road section.

[0010] Preferably, 0<A%<50%, 50%<B%<100%.

[0011] More preferably, A%=15%, B%=85%.

[0012] According to one aspect of the present invention, the following steps are included when conducting a plurality of road section surveys: SH1. Consider multiple road sections as a whole, and penetrate all road sections with the grid interval as the bandwidth to obtain road section slices.

[0013] According to one aspect of the present invention, the investigation method further comprises the following steps: SI1. For the road section to be tested, the vehicle pre-classification is performed to produce the results of the investigation on the working conditions of manually driven vehicles; SI2. Analyze the optimal solution for manually driven vehicle operating conditions based on the results of the manually driven vehicle operating condition survey; SI3. Use the results of the investigation on the working conditions of manually driven vehicles and the optimal solution for the working conditions of manually driven vehicles as the pre-input of the AI ​​big model to further obtain the global optimal solution.

[0014] Beneficial effects of the present invention: (1) When the width of the road section slice is 1.6 meters, the national road network is estimated to be 5.4368 million kilometers. The present invention will be equivalent to obtaining about 3.4 billion road sections. This is equivalent to deploying 3.4 billion survey teams across the country. In the prior art, each survey team has an average of 3 investigators. The use of this technology is equivalent to 10.2 billion investigators monitoring continuously for 24 hours. Conventional survey methods are absolutely impossible to complete the survey of 3.4 billion road sections of the entire road network. The national road network is divided into 3.4 billion road sections under unified rules to cover all roads. Then, using the longitude and latitude data contained in the automobile big data, each reported data of the vehicle is mapped to a unique road section, which is equivalent to completing the field observation action of a vehicle in the conventional survey method. In the present invention, the time and speed are obtained from the message data, and the vehicle type is obtained from the vehicle static information prepared by the platform. Therefore, only two days of data accumulation is needed to complete the field survey of a conventional survey method. This solved the technical problem that "conventional survey methods cannot complete the survey of the billions of road sections in my country's current road network."

[0015] (2) The monitoring platform used in the present invention can operate continuously and can conduct uninterrupted and continuous surveys on 3.4 billion road sections, thereby solving the technical problem that "conventional survey methods cannot conduct unlimited continuous surveys on road sections."

[0016] (3) Based on the present invention, automobile big data can be used to investigate the internal working parameters of vehicles on specific road sections. Conventional investigation methods can only observe three items: sampling time (vehicle passing time), longitude and latitude (by looking up the longitude and latitude coordinates of the investigated section through a map), and vehicle speed. Based on the present invention, all working parameters can be included in the statistical scope during execution. This solves the technical problem that "conventional investigation methods cannot obtain the internal working parameters of vehicles (such as torque, speed, etc.)".

[0017] (4) In recent years, AI large models have been rapidly promoted in the automotive industry. How to convert massive amounts of real vehicle data into effective input for AI model training is an important technical direction and an important economic growth point. The present invention can map road sections into virtual vehicles. Through the collection and processing of road and vehicle operating information by the present invention, a continuous high-quality training data source can be provided for AI, which is very suitable for semi-supervised learning of machines. Based on the implementation of the present invention, when the amount of data on the section is large enough, the section can be equivalent to a virtual vehicle. On this basis, a series of vehicle analysis methods and analysis tools accumulated by the automotive industry for a hundred years can be translated to the road section. Taking the engine universal characteristics as an example, when the number of vehicles passing through the section is large enough, a universal characteristic diagram for this road section can be made: for different vehicle types, only a simple standardized conversion of torque and speed is required to obtain a road section universal characteristic diagram with the road section as the unit and completely consistent in size. This diagram can be used as high-quality training data for AI machine learning. With the help of important semi-supervised learning methods in machine learning, combined with the very mature image neural network in the AI ​​field, a clear and implementable method is provided for vehicle-level artificial intelligence. Following the idea of ​​regarding a road section as a virtual vehicle, the road section can be further regarded as a living organism that grows, ages, gets sick and dies. The time series generated during its entire life cycle can also be converted into digital images as effective training data for the AI ​​large model. This solves the technical problem that "conventional survey results are mainly based on the statistical results of location vehicle distribution and traffic flow, which are difficult to serve as training data for AI models."

[0018] (5) The present invention obtains road section information through the coordinate information fed back by the vehicle, and effectively combines the road section information with the internal operating parameters of the vehicle. This combination can reversely guide the vehicle to safe and optimized driving. The current navigation mainly solves the accessibility problem of how A can reach B, while based on the present invention, the accessibility problem of how A can reach B safely, energy-savingly and efficiently can be solved. Relying on the creative application of automobile big data, the present invention can use cars running on the road to replace people conducting field surveys to build a survey system that covers the entire road network. The present invention uses the advantages of satellite positioning to solve the problem of overall control of global accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0020] Figure 1 is a road surface image drawn according to an embodiment of the present invention; Figure 2 is with Figure 1 Satellite remote sensing images of the same location; Figure 3 is a ground image before denoising according to an embodiment of the present invention; Figure 4 is a ground image after noise removal according to an embodiment of the present invention; Figure 5 It is a schematic diagram of road slices drawn according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The present invention will be further described in detail below in conjunction with the embodiments. The following embodiments are provided to explain the present invention, but the present invention is not limited to the following embodiments.

[0022] Driven by relevant policies, satellite positioning mobile terminals (on-board terminals) are widely installed on cars. For example, the newly produced new energy vehicles and engineering vehicles in China are required to install on-board terminals and upload data. With the addition of tens of millions of vehicles, the collection accuracy has reached the second level, forming an increasingly large data source. At this stage, the application of data is mainly to monitor vehicle form parameters and report abnormal status of vehicles. But more importantly, each frame message of the on-board terminal contains satellite positioning data. At present, all domestic new energy vehicles implement GB / T 32960 "Technical Specifications for Remote Service and Management System of Electric Vehicles". During driving, the satellite positioning data of the vehicle is continuously reported at intervals of about 10 seconds. With the rapid popularization of passenger new energy vehicles, the roads covered almost include all public roads and non-public roads that can be passed.

[0023] The applicant discovered that industry-level satellite positioning data contains ultra-high-precision map road information.

[0024] At present, the minimum resolution of longitude and latitude coordinates uploaded by vehicles can reach 1 / 1000000 (longitude / latitude). The longitude and latitude generated continuously during the driving process of the vehicle are continuously reported in a standard format. If the entire earth's surface is extended into a rectangular plane and regularly divided into grids with a side length of 1 / 1000000 (longitude / latitude), each longitude and latitude reported by the on-board mobile terminal is regarded as an exposure of a grid. With the support of huge amounts of data, the ground space resolution of about 0.1m high-resolution road image (on the longitude, for every 1 degree difference in latitude, the actual distance is about 111 kilometers, so 1 / 1000000 latitude is about 0.1m; on the latitude, for every 1 degree difference in longitude, the actual distance is about 111 kilometers × cosθ, where θ represents the latitude of the latitude. Most of my country’s land is located between 20 and 50 degrees north latitude, cos20°≈0.94, cos50°≈0.64, and 1 / 1000000 longitude here is also about 0.1m). For satellite remote sensing mapping, the highest accuracy claimed in the world is currently 0.5m, and under the limitation of the central projection imaging mechanism, only the center of the image can achieve this accuracy. Calculated in terms of area, the present invention (0.1m×0.1m=0.01m 2 ) is the resolution of satellite remote sensing mapping (0.5m×0.5m=0.25m 2 ) is 25 times.

[0025] The positioning mechanism of the present invention is different from that of remote sensing images. The positioning of the present invention is directly based on the resection of satellites (including Beidou, GPS, GLONASS, and Galileo satellites), which can fundamentally avoid the problem of orthophoto errors and ultimately form an absolute orthophoto image of the road. The present invention combines the satellite navigation system with thousands of vehicles to form a directional scanning system specifically for roads, which can form an array image with strict geographic coordinates. To quickly understand the present invention, you can think of the surface of the earth as a piece of copy paper. When a car goes over one point at a time, road information will be formed on the copy paper. Compared with the overall shooting of optical remote sensing, the present invention only shoots the road, which is conducive to accurate identification of the road on the one hand, and on the other hand, it is conducive to removing noise images. Furthermore, the present invention can be applied not only to vehicle roads, but also to the production of airways for aircraft and ships.

[0026] In order to facilitate those skilled in the art to implement the present invention, the related technologies related to the present invention and the upstream and downstream of the present invention are now described. The following technologies are not necessarily interdependent, but can be superimposed and combined, or each can be implemented separately in combination with other existing technologies. Related technologies include Technology A: Driving direction division technology; Technology B: Spatial indexing and map segmentation technology; Technology C: Frequency-based image corrosion and denoising technology; Technology D: Road image and optical remote sensing image synthesis technology; Technology E: Road section segmentation technology based on driving direction; Technology F: Road section vehicle condition investigation technology; Technology G: Percentile data expression technology; Technology H: Overall accuracy control technology from horizontal to edge and vertical to bottom; Technology I: AI global optimization technology; Technology J: Classification optimization technology.

[0027] Technology A: Driving direction division technology According to the relative position of the longitude and latitude coordinates of the front and rear frames of the same vehicle, the driving azimuth on each grid is calculated, and the roads are classified into four directions according to the driving azimuth. The specific method is to construct a rectangular coordinate system with the longitude and latitude of the vehicle's current frame as the origin. The vertical axis of the coordinate system coincides with the longitude, with north at the top and south at the bottom, and the horizontal axis coincides with the latitude, with east on the right and west on the left. The coordinate plane is divided into four directions according to the direction angle, [315,45), [45,135), [135,225), [225,315) or (315,45], (45,135], (135,225], (225,315], which represent the four directions of north, east, south and west respectively. According to the position of the next frame of the current frame of the vehicle falling into the plane, the driving direction of the current road grid of the vehicle is uniquely determined and recorded. For the longitude and latitude points that also report the GPS (GPS in a broad sense includes Beidou, GLONASS, and Galileo) azimuth, the driving direction of the current road grid of the vehicle can be uniquely determined according to the GPS azimuth and recorded.

[0028] The core function of the driving direction division technology is to add an indicator with clear classification significance to the indifferent longitude and latitude coordinate points. Driving direction is the most common feature in the vehicle-road community. It has nothing to do with the vehicle category, but is strongly related to the vehicle's working conditions. For example, on steep slopes, the speed and fuel consumption (up to dozens of times) of vehicles in the upward and downward directions are very different. By processing the longitude and latitude coordinate information collected from the vehicle through the driving direction division technology, it is possible to determine in the navigation system whether the vehicle is in an uphill or downhill state, and then provide predictive reminders for vehicle driving. Driving direction division technology is also an important support for further distinguishing lanes, because most vehicles will not go in the opposite direction, and the vehicle coordinate information can reflect the lane information in which it is located. This technology can also be linked to traffic management rules in subsequent applications.

[0029] Technology B: Spatial indexing and map tiling technology Globality is one of the essential characteristics of maps. Mapping of specific roads in local areas is only meaningful in special application scenarios, such as unmanned mining trucks. In order to achieve the globality of maps, maps need to be indexed. Map framing is to make the size of topographic maps of various scales consistent, facilitate surveying and mapping, avoid re-surveying and missing surveys, and divide the survey area into several small blocks according to certain rules. This technology is a map framing technology solution for road orthophotos formed by combining map framing theory and computer data characteristics.

[0030] Based on the minimum resolution of the longitude and latitude coordinates uploaded by the vehicle, which can reach 1 / 1000000 (longitude / latitude), this technology first multiplies the longitude and latitude coordinate values ​​(decimal degrees) reported by the vehicle by 1000000 and then rounds them up (in specific implementation, rounding does not necessarily have to be multiplied by 1000000, for example, it can also be multiplied by 921600 and any other value that can round the longitude and latitude coordinates). For example, 115°30' east longitude is rounded to 115.500000×1000000=115500000. 115500000 is converted to binary as 00000110 11100010 01100011 11100000, which is stored in 4 bytes in total (1Byte=8bit). Furthermore, the 4 bytes of longitude are divided into a high double byte and a low double byte. The high double byte (00000110 11100010) is converted to decimal 1762, and the low double byte (01100011 11100000) is converted to decimal 25568. The decimal longitude is named X, the decimal value converted from the high double byte of longitude is named Hx, and the decimal value converted from the low double byte of longitude is named Lx. Then the following equation exists between X, Hx, and Lx: X=65536×Hx+Lx(1) Taking 115°30' east longitude as an example, 115500000=65536×1762+25568, where X=115500000, Hx=1762, Lx=25568.

[0031] Similarly, there is the following equation relationship between the decimal latitude Y and the decimal value Hy converted from the high double-byte latitude and the decimal value Ly converted from the low double-byte latitude: Y=65536×Hy+Ly(2) This technology uses Hx_Hy as the basic standard framing scheme for road images. The full name of the map is "Ln_Hx_Hy", where Ln represents the level of the framing, Hx represents the high-order word of longitude, and Hy represents the high-order word of latitude. The framing method in formulas (1) and (2) is named "L16_Hx_Hy", where L16 represents the width of the map, which is 2^16=65536 basic grids. In the L16 framing method, since 1 / 1000000 (longitude / latitude) is approximately 0.1m, each basic grid is approximately equal to 0.1m on the ground. The area of ​​each standard framing is approximately equivalent to 65536×0.1m×65536×0.1m=42949672.96m on the ground. 2 , which is about 43km 2 . Based on the total land area of ​​my country, which is about 9.6 million square kilometers, the total number of maps in China is about 230,000. In the specific implementation, n in Ln can be any positive integer. For example, L15 represents a map width of 2^15=32768 basic grids, which is equivalent to the enlarged map of L16; L17 represents a map width of 2^17=131072 basic grids, which is equivalent to the reduced map of L16.

[0032] Since computers need to convert decimal data into binary operations and finally convert it into decimal data for feedback to users, if decimal data can be associated with binary data, the computer processing power can be greatly improved. The Ln framing method uses 2^n as the framing basis, which can establish a strong association with computer binary, thereby achieving efficient storage and operation, making it easier for computers to process large amounts of data. Among them, when n=16, the Ln framing method can establish a strong association with the lowest resolution of longitude and latitude coordinates 1 / 1000000 (longitude / latitude), further improving the computer's ability to process coordinate data. This technical point splits the four-byte longitude and latitude values ​​into two double-byte words, high and low, and uses the high-order words of longitude and latitude as the framing basis, which fully utilizes the characteristics of computer binary data and is deeply compatible with the classic GIS four-fork spatial indexing technology.

[0033] The size of the points divided by this technology is very suitable for multi-threaded parallel processing. In practice, it can increase data processing efficiency by about 100 times. In terms of hardware, a medium-performance server CPU can be easily configured with more than 100 cores / threads. The well-known parallel computing technology used by relevant technical personnel can achieve a nearly proportional speed increase compared to a single process. In terms of software, taking the commonly used trajectory point road positioning algorithm as an example, under GIS technology, a complex nonlinear algorithm is used, while this technology converts it into a simple linear technology, and ultimately into the most efficient direct addressing for computers.

[0034] Technology C: Frequency-based image erosion denoising technology Affected by factors such as road construction, vehicle parking, and occlusion by buildings or plants, the longitude and latitude data uploaded from the mobile terminal on the vehicle inevitably contain noise, and there are a large number of burrs on the edges around the main roads. Erosion operation is a basic operation in digital image processing, which can effectively remove edge burrs. The standard corrosion algorithm is only for binary black and white images, and only considers spatial relationships without considering frequency relationships. This technology uses the number of repeated occurrences of grid points as an important basis for determining whether it is noise, that is, in a specified spatial area, such as a standard partition defined by spatial indexing and map partitioning technology (technology B), the higher the frequency of the point, the more likely it is to correspond to the real road grid, and the lower the frequency of the point, the more likely it is to be noise.

[0035] When implementing this technology, you can first preprocess the specified map data to obtain the frequency of the first grid, and read the result into the two-dimensional array in the memory. Traverse each pixel, make a sliding frame with a side length of L with the pixel as the center, traverse the frequency of each pixel in the sliding frame, and form a one-dimensional array with a length of 1×L, calculate the total frequency N in the sliding frame, sort the one-dimensional array from small to large, and then sequentially accumulate the number of low frequencies n. When n is greater than or equal to the pre-specified ratio (such as 5%), the corresponding minimum frequency n0 (such as 5%) is used as the basis for distinguishing the noise points of the sliding frame, traverse each pixel in the sliding frame, and pixels with a frequency less than or equal to n0 (such as 5%) are regarded as noise. Figure 3 and Figure 4 This is a ground image obtained through GPS coordinates in automobile big data. The black pixels in the image are noise images. Figure 3 is the original image before denoising, and n0 is 0%. Figure 4 This is the denoised image with n0 set to 5%.

[0036] Compared with conventional noise removal technology, this technology focuses on detailed features such as the geometric relationship between adjacent longitude and latitude coordinates and the logical relationship between speed and displacement. The operation of this technology is extremely simple and efficient, and it is very suitable for large-scale data processing. The image corrosion denoising technology in the prior art uses the number of hit points or the proportion in the sliding window as the basis for noise discrimination. For the noise points in the low-frequency noise area of ​​the original image, because there are many hit points in the sliding window and the proportion is high, they will be mistakenly regarded as valid points and retained, thereby weakening the noise removal effect. The frequency-based image corrosion denoising technology retains the advantages of spatial operations and makes full use of the data characteristics of the satellite positioning terminal: more than 90% of the longitude and latitude are concentrated in the grid below 10%. This technology is equivalent to the 19 rule, that is, the concentration is higher than the 28 rule. In theory, this technology extends the corrosion algorithm from binary images to grayscale images, and the mechanism establishes a connection between low-frequency points and noise points. Relying on the existing domestic automobile big data resources, the inventor has basically completed the road imaging of the national road network.

[0037] Technology D: Road image and optical remote sensing image synthesis technology When the user of this technology has available optical remote sensing image resources, the image cropping and correction algorithm provided by this technology can be used to achieve the synthesis of road images and optical remote sensing images. The preferred steps may include: SD11. Based on the standard framing provided by the spatial index and map framing technology (Technique B), prepare the optical remote sensing image of the corresponding area and scale the optical remote sensing image to the same pixel number as the standard framing; SD21. Use the road image denoised by frequency-based image erosion denoising technique (Technique C) as a template to crop and correct the optical remote sensing image to remove all non-road pixels.

[0038] Figure 1 Road images drawn using longitude and latitude coordinate information from automobile big data. Figure 1 The original image size is 2048*2048 pixels. Figure 1 Each pixel in represents a coordinate value actually uploaded by the vehicle terminal, that is, Figure 1 The distance between adjacent pixels in the . Figure 1 The image drawn reflects a part of a typical overpass, which is different from the actual road section ( Figure 2 ) and can be used for cropping and correction of optical remote sensing images.

[0039] This technology can realize the data fusion of longitude and latitude information of navigation satellites (Beidou, GPS, GLONASS, Galileo) and optical remote sensing satellites in the same WGS84 coordinate system. From the perspective of navigation satellites, surface object information can be added to each longitude and latitude point. From the perspective of optical remote sensing satellites, high-precision image registration and higher-resolution image interpolation can be achieved. In the process of remote sensing image processing with existing technologies, orthorectification and coordinate correction are required. This technology can provide refined reference for remote sensing image correction, making remote sensing image processing more accurate.

[0040] Furthermore, inspired by the synthesis of navigation coordinates and optical remote sensing images proposed by this technology, it is easy to think of the on-board forward-facing camera device that is standard on new energy vehicles. Through the foreground and background separation technology of conventional video processing (separating temporary vehicles, pedestrians, construction, etc. on the roadside), the real optical image map of the ground of the entire road network can be obtained.

[0041] Technology E: Road section segmentation technology based on driving direction The basic idea of ​​road section segmentation is to continuously segment the road into multiple road sections at equal intervals along the direction of the road. Since the direction of the road can be any direction between 0 and 360 degrees in the rectangular plane coordinates, the road cross-section line usually displayed is not a horizontal line or a vertical line, which is not convenient for large-scale rapid calculation on the computer. The essence of road slicing is to regard spatially continuous road grid points in the same latitude band (north-south roads) or the same longitude band (east-west roads) as the same road section. Based on this technology, large-scale national road slicing can be implemented by user programming. This technology combines the actual characteristics of China's roads that are mainly north-south or east-west, and the basic algorithm flow on the computer includes: SE1. Based on the driving direction division technology (technology A), the road direction is divided into four directions of east, south, west and north at 90 degrees, corresponding to the four azimuth intervals of [315,45), [45,135), [135,225), [225,315) or (315,45], (45,135], (135,225], (225,315) on the rectangular plane; SE2. Determine the direction of the vehicle according to the coordinate data of at least two points continuously uploaded by the vehicle terminal, and determine the direction of the road according to the direction of the vehicle; SE3. Using the longitude and latitude coordinate information in the automobile big data, count the longitude and latitude coordinates of multiple vehicles in the same driving direction in the map slice to be investigated; SE4. For the image to be processed, open a two-dimensional matrix of the same size in the computer memory; SE5. For roads in any direction of east, south, west or north, draw a horizontal line on the basic grid with a grid interval of 2^n; SE6. Traverse all grids from left to right along the horizontal line. The first point encountered is regarded as the left boundary point of the section, until a blank grid is encountered as the right boundary point of the section.

[0042] like Figure 5It reflects a northbound road slice with longitude starting from 104.893480 degrees and ending at 104.893501 degrees, and latitude being 31.261430 degrees (in order to facilitate the understanding of the present technology by those skilled in the art, the slice only traverses one row of grids, and in actual production, all grids are traversed with a grid interval of 2^n). In the figure, the length of the map sheet is 104893505-104893475=30 basic grids, and the width of the map sheet is 31400000-30000000=1400000 basic grids, each basic grid is equivalent to 1 / 1000000 (longitude / latitude), and the grid interval is 2^n. In actual operation, different grid interval values ​​can be selected depending on the accuracy of section segmentation. For example, when n=4, that is, a road slice is made every 16 basic grids, that is, when the bandwidth of the longitude or latitude band is 16 / 1000000 degrees, it is equivalent to the width of the road section slice being 1.6 meters. The national road network is estimated to be 5.4368 million kilometers. Through this technology, about 3.4 billion road sections will be equivalent.

[0043] Technology F: Road section vehicle operating condition survey technology The various vehicle operating indicators in the vehicle message, such as vehicle speed, torque, and rotation speed, are regarded as a sampling survey of the section. In the manual section survey, because the section is fixed, only the actual measured value of the section can be observed, and the change cannot be observed.

[0044] Taking vehicle speed as an example, manual cross-section surveys can only observe average speed, but not acceleration. On a normal driving road, acceleration is strongly correlated with the slope of the road, and acceleration can invert the road slope. Using the characteristics of continuous sampling, automobile big data can successfully solve the cross-section acceleration observation problem.

[0045] Taking fuel flow (in liters per hour) as an example, it is impossible to obtain the fuel flow and change when the vehicle passes the current section through manual section survey. However, fuel flow and change are strongly correlated with energy conservation and emission reduction, and are the key data basis for PCC predictive energy-saving technology. Automotive big data can solve this problem.

[0046] Similarly, engine output torque and engine speed parameters are very important for vehicle operation, especially intelligent driving.

[0047] Technology G: Percentile data expression technology Under big data conditions, especially for urban roads with high-density vehicles, the same road section will quickly accumulate thousands of message records. This technology uses cumulative quantiles to represent individual indicators on each section. Taking vehicle speed as an example, for example, all vehicle speed information obtained on the road section over a period of time can be summarized, and the driving speed in the 15% to 85% quantile speed range on the road section is used as the safe driving speed range, which can be used to directly guide vehicle safe driving. And the experience-level medium-speed driving can be directly quantified as the speed range of a specific road section. The so-called "experience level" refers to the common experience of countless drivers "medium-speed driving", and the specific number of kilometers per hour of medium speed can only be experience. The 15%-85% quantile speed range is a definition in traffic engineering. The basis is: vehicles traveling in this speed range have the lowest accident rate.

[0048] Technology H: Overall precision control technology from side to side and from top to bottom If the road grid is divided into rectangular blocks, and the road sections within the rectangular blocks are segmented and the section conditions are counted, there will be serious edge effect problems, which will be shown as obvious deviations between the roads at the edge of the region and the actual situation when mapping. This technology adopts a horizontal to edge and vertical to bottom solution. For example, the national road network can be regarded as a whole, and the grid interval value of the road section segmentation in the road section segmentation technology based on the driving direction (Technology E) is used as the bandwidth. For north-south roads, for example, a latitude band with a bandwidth of 1.6 meters can be selected as a sub-whole to run through the country from west to east. For east-west roads, a longitude band with a bandwidth of 1.6 meters can be selected as a sub-whole to run through the country from south to north, fundamentally overcoming the edge effect problem of the block processing strategy.

[0049] Technology I: AI global optimization technology By pre-classifying the vehicles, we can produce the most fuel-efficient section survey results, the highest transportation efficiency section survey results, etc. These results are equivalent to the optimal solution for manual driving at the section level. Based on this, the optimal solution for manual driving of spatially continuous road sections can be used as the pre-input of the AI ​​large model to further obtain the global optimal solution. A typical application example is: starting from the current road section, the speed of several subsequent road sections is smoothed, and the proposed results can be used as the speed control parameters for the vehicle to drive more smoothly on a certain section of road. Taking the kinetic energy change as an example, the car starts from the garage, performs a transportation task, and returns to the garage. It is regarded as a transportation process. The entire transportation process is divided into one-second time intervals. The vehicle's kinetic energy per second can be directly calculated according to the kinetic energy formula in physics. The arithmetic sum of the kinetic energy changes of the car in adjacent seconds is equal to zero. Half of the absolute value of the sum of the kinetic energy changes of the car in adjacent seconds can be used to approximate the total energy output of the car in a transportation process, and the energy output directly comes from the consumption of on-board energy. Important off-cycle energy-saving technologies in the modern automobile industry, such as PCC technology, ensure that the speed of the adjacent sections is smooth as much as possible while ensuring the medium speed range of the current section, that is, to make the speed curve smoother on the coordinate plane with time as the horizontal axis and vehicle speed as the vertical axis. The calculation results of automobile big data show that the absolute value of the total kinetic energy change decreases by about 30% by using the sliding average of the speed before and after 5 seconds.

[0050] Technique J: Classification Optimization Technique In a broad sense, the road is one of the factors with a clear classification effect in the complex complex of people, cars, roads and environment. Similarly, the factors with a clear classification effect in the automobile industry are as follows: longitude and latitude, corresponding to the common sense that cars are road transportation tools; driving direction, corresponding to the common sense of driving on the right, prohibiting reverse driving, etc.; vehicle speed, corresponding to the common sense of medium-speed driving, maximum speed limit, etc.; fuel consumption per 100 kilometers, corresponding to the common sense of throttle size; from the speed down, the relationship between indicators such as fuel consumption per 100 kilometers, torque, speed, intake volume, total current, total voltage, etc. and the specific model is getting closer and closer, and the transportation tasks performed by the vehicle are getting closer and closer. This requires that the technical personnel in this field should first complete the vehicle classification in the specific implementation, and then introduce the road section survey implementation of the classified operation data of the same type of vehicles. It should be emphasized that in the specific implementation, it is best to first complete the steps of road section segmentation technology based on driving direction (technology E), and then introduce conventional algorithms such as interpolation in specific vehicle applications so as not to cause obvious deviations from the actual road conditions.

[0051] The basic logic of the present invention is based on a large number of vehicle positioning coordinate points, converting the longitude and latitude coordinates that only have positions but no shape or size into grids that have both positions and shapes and sizes. Since each grid only represents an area of ​​0.01 square meters, sufficient data volume is a basic prerequisite. The basic knowledge and skills of various computers used in the present invention are regarded as the necessary prerequisite capabilities of the users of the present invention.

[0052] The present invention helps technicians to re-understand the essence of the operating condition map: the actual operating condition of the vehicle and its repeatedly proven characteristics and analysis methods can be mapped to the road. That is, the road segment is regarded as a virtual car, and the various operating parameters of the virtual car are reflected by the vehicles passing through the road. This technique greatly narrows the range of the operating parameters of the actual vehicle. The present invention further considers that each car has its own unique characteristics. The core essence of AI lies in the unique characteristics of each car, which is a key point. Traditional probability theory goes from the individual to the population, and AI goes a step further, from the individual to the population and then back to the individual.

[0053] It should be noted that the terms used in the present invention are only for describing specific embodiments, rather than limiting the scope of the present invention. As shown in the present specification, unless the context clearly indicates an exception, the words "one", "a", "a kind of" and / or "the" do not specifically refer to the singular, but may also include the plural. The terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method or device. In the absence of more restrictions, the elements defined by the sentence "include one..." do not exclude the presence of other identical elements in the process, method or device including the elements.

[0054] It should also be noted that the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. Unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", etc. should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be a connection between the two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0055] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

[0056] In addition, it should be noted that the shapes and names of the parts and components of the specific embodiments described in this specification may be different. Any equivalent or simple changes made based on the structure, features and principles described in the patent concept of the present invention are included in the protection scope of the patent of the present invention. The technicians in the technical field of the present invention can make various modifications or supplements to the specific embodiments described or replace them in a similar manner, as long as they do not deviate from the structure of the present invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. A method for road section survey using automobile big data, characterized in that: The following steps are involved: S1. Taking the latitude and longitude of the initial frame data of a single vehicle as the origin, constructing the initial frame rectangular coordinate system, dividing the initial frame rectangular coordinate system into four coordinate azimuth intervals of north, east, south and west; S2. Determine the direction of travel of the vehicle on the road according to the position where the latitude and longitude of a single vehicle in subsequent frame data falls within the coordinate azimuth interval; S3. Using the longitude and latitude coordinate information in the automobile big data, statistics are collected on the longitude and latitude coordinates of multiple vehicles in the same driving direction in the map slice to be investigated; S4. Create a two-dimensional matrix in the computer memory with the same size as the map tiles to be investigated; S5. For roads in any direction of east, south, west or north, draw a horizontal line with a grid interval of 2^n on the basic grid of the survey map; S6. Traverse all grids from left to right along the horizontal line, and regard the first longitude and latitude coordinate point as the left boundary point of the section, until a blank grid is encountered as the right boundary point of the section, and obtain the road section slice.

2. The method for road section investigation according to claim 1, characterized in that: In step S5, n=4.

3. The method for road section investigation according to claim 1, characterized in that: The generation of the coordinate azimuth comprises the following steps: SA1. In the above coordinate azimuth interval, the north direction is between 315° and 45°, the east direction is between 45° and 135°, the south direction is between 135° and 225°, and the west direction is between 225° and 315°.

4. The method for road section investigation according to claim 1, characterized in that: The step S5 further includes the following steps: SF1. The vehicle operating condition indicators in the vehicle message are regarded as a sampling survey of the road section.

5. The method for road section investigation according to claim 4, characterized in that: The step SF1 further includes the following steps: SG1. Express the individual indicators of vehicle operating conditions on the road section as cumulative quantiles.

6. The method for road section investigation according to claim 5, characterized in that: The single indicator in step SG1 is the vehicle speed, and the vehicle speed in the percentile speed interval of A%-B% on the road section is taken as the safe driving speed range of the road section.

7. The method for road section investigation according to claim 6, characterized in that: 0<A%<50%, 50%<B%<100%.

8. The method for road section investigation according to claim 7, characterized in that: A%=15%, B%=85%.

9. The method for road section investigation according to claim 1, characterized in that: The following steps are involved when conducting a multiple road section survey: SH1. Consider multiple road sections as a whole, and penetrate all road sections with the grid interval as the bandwidth to obtain road section slices.

10. The method for road section investigation according to claim 4, characterized in that: The survey method further comprises the following steps: SI1. For the road section to be tested, the vehicle pre-classification is performed to produce the results of the working condition investigation of the manually driven vehicle; SI2. Analyze the optimal solution for manually driven vehicle operating conditions based on the results of the manually driven vehicle operating condition survey; SI3. Use the results of the investigation on the working conditions of manually driven vehicles and the optimal solution for the working conditions of manually driven vehicles as the pre-input of the AI ​​big model to further obtain the global optimal solution.

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