Train localization method based on sleeper codes

Abstract

A train localization method based on sleeper codes. The method comprises the following steps: formulating an encoding protocol, and on the basis of the encoding protocol, drawing sleeper codes on sleepers along a rail line, and using the sleeper codes as localization targets for train localization; mounting a point cloud acquisition module on a train body, using the point cloud acquisition module to acquire all point cloud data in front of a train in a traveling direction, and sending the point cloud data to a train-mounted computer; the train-mounted computer using a sleeper extraction algorithm to extract target point cloud data of the sleepers from among all the point cloud data; the train-mounted computer performing decoding on the target point cloud data of the sleepers, and converting same into absolute position information of the current localization target of the train, i.e., absolute position information of the sleeper codes; and the train-mounted computer performing comprehensive computation, so as to obtain the current precise absolute position information of the train. By means of the present invention, sleepers are used to carry encoding information, and encoding protocols in any forms are supported, such that high versatility is achieved, and customized designs can be made according to different line situations and different user requirements, allowing easy porting.

Description

A Train Positioning Method Based on Sleeper Coding Technical Field

[0001] This invention relates to the field of rail transit train positioning technology, specifically to a train positioning method based on sleeper coding. Background Technology

[0002] Rail transit safety is crucial to the lives and property of those involved and has always been a key focus of the rail transit industry. Train positioning technology is a fundamental function of rail transit signal control, playing a vital role in ensuring train operation safety and improving operational efficiency. More accurate and robust positioning methods can not only enhance train safety but also further improve the operational efficiency of the entire transportation network.

[0003] In existing technologies, traditional train positioning technologies can be mainly divided into continuous positioning technologies and point positioning technologies. Continuous positioning includes: cumulative positioning based on onboard equipment, GNSS-based positioning, track circuit positioning, and positioning based on Simultaneous Localization and Mapping (SLAM). Point positioning includes: beacon positioning, kilometer marker positioning, ultra-wideband (UWB) positioning, and secondary radar positioning.

[0004] However, the aforementioned vehicle positioning schemes suffer from problems such as high cost of additional equipment, poor versatility, low accuracy, difficulty in eliminating accumulated errors, and poor safety. Therefore, it is essential to design a train positioning method that overcomes these technical problems.

[0005] It is understood that the above statements only provide background information related to the present invention and do not necessarily constitute prior art. Summary of the Invention

[0006] The purpose of this invention is to provide a train positioning method based on sleeper coding, which is simple to implement, has extremely high positioning accuracy, and can achieve autonomous train positioning across the entire line.

[0007] To achieve the above objectives, this invention provides a train positioning method based on sleeper coding, comprising the following steps: S1, formulating a coding protocol and drawing sleeper codes on the sleepers along the track line according to the coding protocol, serving as positioning targets for train positioning; S2, installing a point cloud acquisition module on the train body, using the point cloud acquisition module to acquire all point cloud data ahead of the train and sending it to the train's onboard computer; S3, the train's onboard computer using a sleeper extraction algorithm to extract sleeper target point cloud data from the all point cloud data; S4, the train's onboard computer decoding the sleeper target point cloud data and converting it into the absolute position information of the train's current positioning target, i.e., the absolute position information of the sleeper code; S5, the train's onboard computer performing comprehensive calculations to obtain the precise absolute position information of the current train.

[0008] Preferably, the encoding protocol uses binary, ternary, multi-ary, or patterned representations.

[0009] Preferably, several sets of sleeper codes are spaced apart on the sleepers along the track line, and each set of sleeper codes is composed of multiple adjacent sleepers.

[0010] Among them, multiple adjacent sleepers carrying sleeper codes constitute a sleeper group, and each sleeper in the sleeper group represents a different code bit of the sleeper code.

[0011] Preferably, in S2, the point cloud acquisition module detects all targets in front of the train by emitting detection signals, obtains the distance information of each target, and converts it into position coordinates to form the currently detected point cloud data.

[0012] Preferably, in S2, the point cloud acquisition module uses millimeter-wave radar, lidar, or a depth camera.

[0013] Preferably, the millimeter-wave radar emits electromagnetic wave signals to detect targets, the lidar emits laser beams to detect targets, and the depth camera emits infrared light or structured light to detect targets.

[0014] Preferably, in step S3, the sleeper extraction algorithm clusters the sleeper targets carrying sleeper codes in front of the train from all the point cloud data, so that the sleeper targets are separated from other targets, and extracts the sleeper target point cloud data that does not contain other irrelevant point cloud data and only carries the sleeper codes.

[0015] Preferably, the decoding principle is compatible with the established encoding protocol, and the train's onboard computer decodes the extracted sleeper target point cloud data according to the encoding protocol established in S1.

[0016] Preferably, in S5, the precise absolute position information of the current train = the absolute position information of the sleeper code - the distance measurement information of the point cloud data.

[0017] Preferably, the absolute position of the sleeper code is the position of the first sleeper closest to the train in the sleeper group corresponding to the sleeper code; the distance measurement information of the point cloud data is the distance between the point cloud acquisition module and the absolute position of the sleeper code.

[0018] Preferably, in step S1, the code is drawn on the sleeper by spraying with a high-reflectivity material or a sticker.

[0019] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the train positioning method based on sleeper coding.

[0020] The present invention also provides an electronic device, including a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, it implements the train positioning method based on sleeper coding.

[0021] In summary, compared with the prior art, the train positioning method based on sleeper coding provided by the present invention has at least the following beneficial effects:

[0022] (1) This invention can realize autonomous train positioning across the entire line, enabling segmented train positioning in any section of the entire line, including tunnels, elevated tracks, turnouts, and platforms, without relying on any external equipment or signal support. It can still function normally when CC, BLS, or GNSS fail.

[0023] (2) The algorithm of this invention is simple to implement, requires low computing power, and can guarantee real-time computing performance;

[0024] (3) The present invention has no cumulative error and high positioning accuracy. The theoretical positioning accuracy depends only on the ranging accuracy of the point cloud.

[0025] (4) The present invention has high repositioning efficiency. When the train loses its position due to abnormality, malfunction or other reasons, the train's position can be quickly regained in one go by identifying the next sleeper code information.

[0026] (5) This invention innovatively utilizes sleepers to carry coded information and supports any form of coding protocol. It has high versatility and can be customized according to different line conditions and user needs. For different cities and different lines, the solution has high reusability and is easy to transplant. Attached Figure Description

[0027] Figure 1 is a flowchart of the train positioning method based on sleeper coding in this invention;

[0028] Figure 2 is a schematic diagram of the train positioning method based on sleeper coding in this invention;

[0029] Figure 3 shows an embodiment of the sleeper coding method for train positioning based on sleeper coding in this invention;

[0030] Figure 4 shows another embodiment of the sleeper coding method for train positioning based on sleeper coding in this invention. Detailed Implementation

[0031] The present invention will be further described below with reference to Figures 1-4, by detailing a preferred embodiment.

[0032] It should be noted that the accompanying drawings are in a very simplified form and use non-precise proportions. They are only used to facilitate and clarify the purpose of illustrating the embodiments of the present invention, and are not intended to limit the implementation conditions of the present invention. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportional relationship, or adjustments to the size should still fall within the scope of the technical content disclosed in the present invention, provided that they do not affect the effects and objectives that the present invention can produce.

[0033] It should be noted that, in this invention, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only the expressly listed elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0034] As shown in Figure 1, the present invention provides a train positioning method based on sleeper coding, comprising the following steps:

[0035] S1. Develop a coding protocol and draw the sleeper codes on the sleepers along the track line according to the coding protocol, which will serve as positioning targets for train positioning;

[0036] S2. Install a point cloud acquisition module on the train body, use the point cloud acquisition module to acquire all point cloud data in front of the train, and send it to the train's onboard computer.

[0037] S3. The train's onboard computer uses a sleeper extraction algorithm to extract sleeper target point cloud data from all the point cloud data.

[0038] S4. The train's onboard computer decodes the sleeper target point cloud data and converts it into the absolute position information of the train's current positioning target, i.e., the absolute position information of the sleeper code.

[0039] S5. The train's onboard computer performs comprehensive calculations to obtain the precise absolute position information of the current train.

[0040] Furthermore, in S1, the encoding protocol is a custom rule, which can be expressed in binary, ternary, multi-ary, or in the form of special patterns or any other custom encoding format that can be used as a special identifier.

[0041] Furthermore, several sets of sleeper codes are spaced apart on the sleepers along the track line, and each set of sleeper codes is composed of multiple adjacent sleepers. Therefore, only when multiple adjacent sleepers work together can a complete set of sleeper codes be displayed. Among them, multiple adjacent sleepers carrying sleeper codes constitute a sleeper group, and each sleeper in the sleeper group represents a different code bit of the sleeper code.

[0042] As shown in Figure 3, in this embodiment, the encoding protocol adopts a binary form. A complete sleeper code includes one start bit, eight sleeper code bits, one even parity bit, and one end bit. It can be understood that in this encoding protocol, each sleeper code consists of 11 (1+8+1+1) sleepers, thus these 11 sleepers constitute a sleeper group. As shown in Figure 4, in this embodiment, the encoding protocol adopts a ternary form. A complete sleeper code includes only 6 sleeper code bits. It can be understood that in this encoding protocol, each sleeper code consists of 6 sleepers, thus these 6 sleepers constitute a sleeper group.

[0043] It is understood that, in this embodiment of the invention, the sleeper code on the sleeper can serve as a reference for train positioning, similar to the role played by road signs on highways.

[0044] Furthermore, in S2, the point cloud acquisition module can employ point cloud data detection devices such as millimeter-wave radar, lidar, depth cameras, or any other detectors capable of acquiring point cloud data. It detects all targets ahead of the train by emitting detection signals, obtains the distance information of each target, and converts it into point cloud data in a Cartesian coordinate system. That is, all the obtained point cloud data represents the position and shape of each target in three-dimensional coordinates, thus providing a basis for subsequent processing and analysis. The targets ahead of the train include sleepers, buildings, the background environment, and all other detectable targets.

[0045] The millimeter-wave radar emits electromagnetic wave signals to detect targets, the lidar emits laser beams to detect targets, and the depth camera emits infrared light or structured light to detect targets.

[0046] Furthermore, in S3, the sleeper extraction algorithm can cluster the sleeper targets carrying sleeper codes in front of the train from all the point cloud data, so that the sleeper targets are separated from other targets, and extract the sleeper target point cloud data that contains only sleeper codes and does not contain other irrelevant point cloud data.

[0047] Furthermore, in S4, the decoding principle is adapted to the established encoding protocol, and the train's onboard computer decodes the extracted sleeper target point cloud data according to the encoding protocol established in S1.

[0048] Furthermore, in S5, the precise absolute position information of the current train = the absolute position information of the sleeper code - the distance measurement information of the point cloud data.

[0049] Understandably, during decoding, a complete set of sleeper codes needs to be decoded to determine its validity. Therefore, after decoding, the position of the first sleeper closest to the train in the sleeper group corresponding to that set of sleeper codes is the absolute position of that set of sleeper codes. Similarly, as shown in Figure 2, the distance information x in the point cloud data is the distance between the point cloud acquisition module and the position of the first sleeper closest to the train in the sleeper group corresponding to the sleeper code.

[0050] In one embodiment of the present invention, as shown in Figure 3, an encoding protocol is first established. This encoding protocol adopts a binary form, including one start bit, eight sleeper encoding bits, one even parity bit, and one end bit. Secondly, according to this encoding protocol, high-reflectivity material / stickers are used to draw the sleeper codes on the sleepers along the track line to distinguish the start bit, end bit, sleeper encoding bit 0, and sleeper encoding bit 1. In this embodiment, normal sleepers are not painted; the start and end bits are painted; sleeper encoding bit 0 is painted on the left half in front of the train's movement; and sleeper encoding bit 1 is painted on the right half in front of the train's movement. Next, the point cloud acquisition module acquires all point cloud data in front of the train's movement. It is understood that the positions where high-reflectivity material / stickers are painted will have higher reflectivity and can be clearly distinguished in the point cloud. Then, the sleeper extraction algorithm can extract a complete sleeper code (bits 1+8+1+1) and perform parity checking. Finally, the train's onboard computer decodes the sleeper's code and calculates the train's precise absolute position information by combining the distance measurement information from the point cloud data.

[0051] It should be noted that the materials used for railway sleepers include wood and concrete, and the number of sleepers generally ranges from 1440 to 1920 per kilometer. In this embodiment, based on 1667 sleepers per kilometer and a 60cm spacing between every two adjacent sleepers, each complete sleeper code corresponds to 11 sleepers, the sleeper code length = 10 × 60cm = 6m, and the sleeper code capacity = 2. 8 =256, meaning it can represent 256 different locations. Understandably, for a 40km long route, if the sleeper coding locations are evenly distributed, the interval between any two adjacent sleeper codes is approximately 156m (40km / 256). This positioning density is sufficient for the train's positioning needs.

[0052] To further increase the positioning density, in addition to increasing the number of sleeper coding bits, the sleeper coding depth can also be increased to improve the sleeper coding capacity for the same length.

[0053] In another embodiment of the present invention, as shown in Figure 4, the sleeper code eliminates the start bit, end bit, and parity bit, but changes the encoding protocol to a ternary form. This not only shortens the required number of bits but also increases the sleeper code capacity. Specifically, this encoding protocol includes only 6 sleeper code bits, which are also coated with a high-reflectivity material. Normal sleepers are not coated; the left half of the sleeper code bit 0 is coated, the right half of the sleeper code bit 1 is coated, and the entire sleeper code bit 2 is coated. Therefore, for this encoding protocol, the sleeper code length = 5 × 60 cm = 3 m, and the sleeper code capacity = 3. 6 =729, meaning it can represent 729 different locations. Similarly, for a 40km long route, if the sleeper coding positions are evenly distributed, then the adjacent interval between any two adjacent sleeper codes is approximately 55m = 40km / 729. It can be understood that, in this embodiment, by shortening the sleeper coding length, not only is the sleeper coding capacity increased, but the extraction and decoding efficiency of sleeper targets is also improved, and the positioning density of 55m intervals is sufficient to meet the positioning granularity requirements of the train.

[0054] Furthermore, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described train positioning method based on sleeper coding.

[0055] Furthermore, the present invention also provides an electronic device, including a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the above-described train positioning method based on sleeper coding is implemented.

[0056] In summary, this invention provides a train positioning method based on sleeper coding, which enables autonomous train positioning across the entire railway line, including tunnels, elevated sections, switches, and platforms, without relying on any external equipment or signal support. It maintains normal functionality even when CC, BLS, or GNSS fail. The algorithm is simple to implement, requires low computing power, and ensures real-time computation. It exhibits no cumulative error, high positioning accuracy (theoretically, positioning accuracy depends only on the distance measurement accuracy of the point cloud), and high repositioning efficiency. When a train loses its position due to anomalies or malfunctions, its position can be quickly regained by recognizing the next coded information. The innovative use of sleepers to carry coded information, supporting any form of coding protocol, offers high versatility. It can be customized to different line conditions and user needs, and the solution is highly reusable and easy to port across different cities and lines.

[0057] Although the present invention has been described in detail through the preferred embodiments above, it should be understood that the above description should not be considered as a limitation of the present invention. Various modifications and substitutions to the present invention will be apparent to those skilled in the art after reading the above description. Therefore, the scope of protection of the present invention should be defined by the appended claims.

Claims

1. A train positioning method based on sleeper coding, characterized in that, Includes the following steps: S1. Develop a coding protocol and draw the sleeper codes on the sleepers along the track line according to the coding protocol, which will serve as positioning targets for train positioning; S2. Install a point cloud acquisition module on the train body, use the point cloud acquisition module to acquire all point cloud data in front of the train, and send it to the train's onboard computer. S3. The train's onboard computer uses a sleeper extraction algorithm to extract sleeper target point cloud data from all the point cloud data. S4. The train's onboard computer decodes the sleeper target point cloud data and converts it into the absolute position information of the train's current positioning target, i.e., the absolute position information of the sleeper code. S5. The train's onboard computer performs comprehensive calculations to obtain the precise absolute position information of the current train.

2. The train positioning method based on sleeper coding as described in claim 1, characterized in that, The encoding protocol uses binary, ternary, multi-ary, or pattern.

3. The train positioning method based on sleeper coding as described in claim 2, characterized in that, Along the track line, there are several sets of sleeper codes spaced apart, and each set of sleeper codes is composed of multiple adjacent sleepers. Among them, multiple adjacent sleepers carrying sleeper codes constitute a sleeper group, and each sleeper in the sleeper group represents a different code bit of the sleeper code.

4. The train positioning method based on sleeper coding as described in claim 1, characterized in that, In S2, the point cloud acquisition module detects all targets in front of the train by emitting detection signals, obtains the distance information of each target, and converts it into position coordinates to form the currently detected point cloud data.

5. The train positioning method based on sleeper coding as described in claim 4, characterized in that, In S2, the point cloud acquisition module uses millimeter-wave radar, lidar, or a depth camera.

6. The train positioning method based on sleeper coding as described in claim 5, characterized in that, The millimeter-wave radar emits electromagnetic wave signals to detect targets, the lidar emits laser beams to detect targets, and the depth camera emits infrared light or structured light to detect targets.

7. The train positioning method based on sleeper coding as described in claim 1, characterized in that, In S3, the sleeper extraction algorithm clusters the sleeper targets carrying sleeper codes in front of the train from all the point cloud data, so that the sleeper targets are separated from other targets, and extracts the sleeper target point cloud data that does not contain other irrelevant point cloud data and only carries the sleeper code.

8. The train positioning method based on sleeper coding as described in claim 3, characterized in that, The decoding principle is compatible with the established encoding protocol. The train's onboard computer decodes the extracted sleeper target point cloud data according to the encoding protocol established in S1.

9. The train positioning method based on sleeper coding as described in claim 1, characterized in that, In S5, the precise absolute position information of the current train = the absolute position information of the sleeper code - the distance measurement information of the point cloud data.

10. The train positioning method based on sleeper coding as described in claim 9, characterized in that, The absolute position of the sleeper code is the position of the first sleeper closest to the train in the sleeper group corresponding to that sleeper code; The distance measurement information of the point cloud data is the distance between the absolute position of the point cloud acquisition module and the sleeper code.

11. The train positioning method based on sleeper coding as described in claim 3, characterized in that, In S1, the code is drawn on the sleeper by spraying it with a high-reflectivity material or sticker.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the train positioning method based on sleeper coding as described in any one of claims 1 to 11.

13. An electronic device, characterized in that, It includes a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, it implements the train positioning method based on sleeper coding as described in any one of claims 1 to 11.

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