Locomotive automatic driving simulation system and method

Abstract

The invention provides a locomotive automatic driving simulation system and method.The system comprises a simulation module, a sensing module, a dispatching module, a working condition module and a machine control module, a map model and a locomotive model are built through the simulation module, fusion positioning simulation is carried out according to the map model and the locomotive model, locomotive positioning information is obtained, and the locomotive positioning information is sent to the locomotive control module; locomotive operation simulation is carried out through the locomotive model to obtain locomotive train control information; locomotive sensing data is configured through the sensing module; a locomotive dispatching instruction is initiated through the dispatching module; locomotive operation working conditions are configured through the working condition module; locomotive control is carried out by combining at least one of locomotive positioning information, locomotive train control information, locomotive sensing data, locomotive dispatching instructions and locomotive operation conditions, and testing of an automatic driving algorithm is completed; a complex railway operation environment can be simulated, the performance of an automatic driving algorithm can be tested in different operation environments, and locomotive automatic driving simulation testing is achieved.

Description

Technical Field

[0001] The present application relates to the field of locomotive automatic driving technology, and specifically to a locomotive automatic driving simulation system and method. Background Art

[0002] With the continuous development of science and technology, autonomous driving technology is becoming a hot topic in the transportation field. Autonomous driving simulation not only significantly reduces the testing costs of simulation systems, but also achieves a qualitative leap in realism and scalability.

[0003] While autonomous vehicle simulation technology is increasingly mature, it's difficult to apply to locomotive simulation technology due to significant differences in physical and dynamic characteristics, environments, and scenarios. Therefore, how to simulate and test locomotive autonomous driving has become a pressing issue in the field. Summary of the Invention

[0004] In view of the above-mentioned shortcomings of the prior art, the present application provides a locomotive automatic driving simulation system and method to solve the above-mentioned technical problem of how to perform simulation testing on locomotive automatic driving.

[0005] The present application provides a locomotive automatic driving simulation system, which includes: a simulation module for building a map model and a locomotive model, performing fusion positioning simulation based on the map model and the locomotive model to obtain locomotive positioning information, and performing locomotive operation simulation through the locomotive model to obtain locomotive train control information; a perception module for configuring locomotive perception data; a scheduling module for initiating locomotive scheduling instructions; an operating condition module for configuring locomotive operating conditions, wherein the locomotive operating conditions include at least one of driving conditions, geographical conditions and weather conditions; a machine control module for controlling the locomotive based on a preset automatic driving algorithm and in combination with at least one of the locomotive positioning information, the locomotive train control information, the locomotive perception data, the locomotive scheduling instructions and the locomotive operating conditions to complete the test of the automatic driving algorithm.

[0006] In one embodiment of the present application, a map model and a locomotive model are constructed, and a fusion positioning simulation is performed based on the map model and the locomotive model to obtain locomotive positioning information, including: obtaining locomotive railway map information and constructing the map model based on the locomotive railway map information; constructing the locomotive model in the map model according to the physical characteristics and motion characteristics of the locomotive; simulating the position of the locomotive model in the map model according to the coordinates of the locomotive model and the coordinates of the map model to obtain initial positioning information, and obtaining the locomotive positioning information based on the initial positioning information.

[0007] In an embodiment of the present application, obtaining the locomotive positioning information based on the initial positioning information includes: acquiring the locomotive perception data by interacting with the perception module; comprehensively obtaining the locomotive positioning information according to the locomotive perception data and the initial positioning information.

[0008] In an embodiment of the present application, performing locomotive operation simulation through a locomotive model to obtain locomotive train control information includes: acquiring a locomotive control instruction by interacting with the locomotive control module, where the locomotive control module is further configured to initiate the locomotive control instruction; executing the locomotive control instruction through the locomotive model to simulate the locomotive operation state and obtain the locomotive train control information.

[0009] In an embodiment of the present application, configuring the locomotive perception data includes: constructing a virtual environment and a sensor model through the simulation module, and integrating the sensor model and the locomotive model into the virtual environment; simulating the driving scenario of the locomotive model in the virtual environment, and collecting data in the driving scenario through the sensor model to obtain the locomotive perception data.

[0010] In an embodiment of the present application, initiating a locomotive dispatching instruction includes: acquiring locomotive driving information by interacting with the locomotive control module, and acquiring car body perception data by interacting with the perception module, where the locomotive control module is further configured to generate the locomotive driving information, and the perception module is further configured to configure the car body perception data; sending a microcomputer interlocking instruction to the simulation module according to the locomotive driving information and the car body perception data to obtain the microcomputer interlocking information fed back by the simulation module, where the simulation module is further configured to build a ground equipment model, and in response to the microcomputer interlocking instruction, perform ground equipment operation simulation through the ground equipment model to generate the microcomputer interlocking information; initiating the locomotive dispatching instruction according to the microcomputer interlocking information.

[0011] In an embodiment of the present application, ground equipment operation simulation is performed through the ground equipment model to generate the microcomputer interlocking information, including: performing barrier machine operation simulation through the barrier machine model to generate barrier machine status information, where the barrier machine status information includes barrier machine normal operation status information or barrier machine fault status information, and the ground equipment model includes the barrier machine model; performing signal machine operation simulation through the signal machine model to generate signal machine status information, where the signal machine status information includes signal machine normal display status information or signal machine abnormal status information, and the ground equipment model includes the signal machine model; performing turnout operation simulation through the turnout model to generate turnout status information, where the turnout status information includes turnout normal conversion status information or turnout fault status information, and the ground equipment model includes the turnout model; generating the microcomputer interlocking information based on at least one of the barrier machine status information, the signal machine status information, and the turnout status information.

[0012] In an embodiment of the present application, the working condition module is further configured to configure the car body PLC information; the working condition module interacts with the simulation module according to the car body PLC instruction to generate the car body PLC information and feedback it to the dispatching module, where the dispatching module is further configured to initiate the car body PLC instruction, and the simulation module is further configured to build a car body model and perform car body action simulation through the car body model in response to the car body PLC instruction.

[0013] In an embodiment of the present application, the working condition module is further configured to configure the locomotive PLC information; the working condition module interacts with the simulation module according to the locomotive PLC instruction to generate the locomotive PLC information and feedback it to the locomotive control module, where the locomotive control module is further configured to initiate the locomotive PLC instruction, and the simulation module is further configured to perform locomotive action simulation through the locomotive model in response to the locomotive PLC instruction.

[0014] In an embodiment of the present application, a locomotive automatic driving simulation method is further provided, which is applied to the locomotive automatic driving simulation system as described above. The method includes: performing fusion positioning simulation and locomotive operation simulation respectively through the simulation module to obtain locomotive positioning information and locomotive train control information; configuring locomotive perception data through the perception module; initiating a locomotive dispatching instruction through the dispatching module; configuring locomotive operation conditions through the working condition module; and performing locomotive control through the locomotive control module based on a preset automatic driving algorithm and in combination with at least one of the locomotive positioning information, the locomotive train control information, the locomotive perception data, the locomotive dispatching instruction, and the locomotive operation conditions to complete the test of the automatic driving algorithm.

[0015] Advantages of the present invention: The present invention provides a locomotive automatic driving simulation system and method. The system performs integrated positioning simulation and locomotive operation simulation through a simulation module to generate locomotive positioning information and locomotive train control information, configures locomotive perception data through a perception module, provides locomotive dispatching instructions through a dispatching module, and sets locomotive operation conditions through a working condition module. It can simulate complex railway operation environments so that the locomotive control module can test or verify the performance of the automatic driving algorithm under different operation environments, realizing locomotive automatic driving simulation testing. The system has high flexibility and scalability, can adapt to different test requirements, and provides strong support for the development or optimization of locomotive automatic driving technology.

[0016] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and do not limit this application. Brief Description of the Drawings

[0017] Figure 1 is a block diagram of a locomotive automatic driving simulation system shown in an exemplary embodiment of the present application;

[0018] Figure 2 is a flowchart of locomotive physical object modeling shown in a specific embodiment of the present application;

[0019] Figure 3 is a flowchart of locomotive longitude and latitude calculation shown in a specific embodiment of the present application;

[0020] Figure 4 is a schematic diagram of laser distance positioning simulation shown in a specific embodiment of the present application;

[0021] Figure 5 is a schematic diagram of two-dimensional code recognition positioning simulation shown in a specific embodiment of the present application;

[0022] Figure 6 is a flowchart of tanker physical object modeling shown in a specific embodiment of the present application;

[0023] Figure 7 is a schematic diagram of obstacle simulation during locomotive driving shown in a specific embodiment of the present application;

[0024] Figure 8 is a schematic diagram of multi-locomotive task level division simulation shown in a specific embodiment of the present application;

[0025] Figure 9 is a schematic diagram of weather simulation shown in a specific embodiment of the present application;

[0026] Figure 10 is an architecture diagram of a locomotive automatic driving simulation system shown in a specific embodiment of the present application;

[0027] Figure 11 It is a flowchart showing the response of the simulation platform to the microcomputer interlocking instruction shown in a specific embodiment of the present application;

[0028] Figure 12 It is a flowchart showing the transmission of the tanker perception data shown in a specific embodiment of the present application;

[0029] Figure 13 It is a flowchart showing the transmission of the locomotive perception data shown in a specific embodiment of the present application;

[0030] Figure 14 It is a schematic diagram showing the perception obstacle simulation shown in a specific embodiment of the present application;

[0031] Figure 15 It is a schematic diagram showing the response of the simulation platform to the locomotive PLC instruction shown in a specific embodiment of the present application;

[0032] Figure 16 It is a schematic diagram showing the response of the simulation platform to the tanker PLC instruction shown in a specific embodiment of the present application. Detailed implementation manners

[0033] The following uses specific specific examples to illustrate the implementation manners of the present application. Those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in this specification. The present application can also be implemented or applied through other different specific implementation manners. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0034] It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present application in a schematic manner. Therefore, only the components related to the present application are shown in the diagrams, rather than being drawn according to the number, shape, and size of the components in actual implementation. The type, quantity, and ratio of each component in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0035] It should be noted that in the present application, "first", "second", etc. are only used to distinguish similar objects, and are not used to limit the order or sequence of similar objects. The described "including", "having", etc. are deformed, indicating that the scope covered by the subject of this word is not exclusive except for the examples shown by this word.

[0036] It can be understood that the various digital numbers, step numbers, etc. recorded in the present application are for the convenience of description and are not used to limit the scope of the present application. The size of the reference numbers in the present application does not mean the sequence of execution order. The execution order of each process should be determined by its function and internal logic.

[0037] In the following description, a large number of details are explored to provide a more thorough explanation of the embodiments of the present application. However, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present application difficult to understand.

[0038] Embodiments of the present application respectively propose a locomotive automatic driving simulation system and a locomotive automatic driving simulation method, and the following will describe these embodiments in detail.

[0039] Please refer to Figure 1 , Figure 1 which is a block diagram of a locomotive automatic driving simulation system shown in an exemplary embodiment of the present application.

[0040] As Figure 1 shown, the exemplary locomotive automatic driving simulation system includes:

[0041] A simulation module 110, configured to build a map model and a locomotive model, perform fusion positioning simulation according to the map model and the locomotive model to obtain locomotive positioning information, and perform locomotive operation simulation through the locomotive model to obtain locomotive train control information; a perception module 120, configured to configure locomotive perception data; a scheduling module 130, configured to initiate a locomotive scheduling instruction; a working condition module 140, configured to configure the locomotive operation working condition, where the locomotive operation working condition includes at least one of a driving working condition, a geographical working condition, and a weather working condition; a locomotive control module 150, configured to perform locomotive control based on a preset automatic driving algorithm and in combination with at least one of the locomotive positioning information, the locomotive train control information, the locomotive perception data, the locomotive scheduling instruction, and the locomotive operation working condition, and complete the test of the automatic driving algorithm.

[0042] In this embodiment, the locomotive positioning information includes information such as the coordinate information of the locomotive, the distance or relative position information between the locomotive and other objects. The locomotive train control information includes control instructions and status monitoring information, where the control instructions include overcharge, operation, first gear braking, second gear braking, third gear braking, fourth gear braking, excessive decompression, emergency braking, etc., and the status monitoring includes diesel engine / motor speed, motor current / voltage, running kilometers, engine oil temperature, engine oil pressure, fuel pressure, cooling water temperature, cooling water level, main air cylinder pressure, brake cylinder pressure, train pipe pressure, etc.

[0043] The perception module 120 configuring locomotive perception data means that the perception module 120 simulates locomotive on-vehicle perception devices, including cameras, high-definition cameras, lidars, and millimeter-wave radars, etc., to perceive the locomotive surrounding environment, automatically generate video, image, and point cloud data as locomotive perception data, providing data support for the locomotive automatic driving decision-making and control of the locomotive control module 150. For example: when obstacles such as people, locomotives, tank cars / flat cars, stones, trees, animals, etc. approach or leave the railway track, the perception module 120 simulates through the locomotive on-vehicle perception devices and gives recognition results, such as the type, distance, size, movement direction, etc. of the obstacles, as locomotive perception data for the locomotive automatic driving judgment of the locomotive control module 150.

[0044] The dispatching module 130 initiating locomotive dispatching instructions means that the dispatching module 130 generates locomotive dispatching instructions such as braking, speed limit, locomotive driving direction, etc. by simulating the dispatching behaviors of an automatically driven locomotive in different scenarios, verifying the efficiency and reliability of the dispatching algorithm, which is the core component of the automatic driving simulation test, including functions such as locomotive path planning, task allocation, traffic flow management, etc.

[0045] The working condition module 140 configuring locomotive operating conditions means that the working condition module 140 generates locomotive operating conditions such as driving conditions, geographical conditions, and weather conditions through driving scenario simulation, geographical environment simulation, weather environment simulation, etc. Among them, the driving conditions include at least one of the locomotive speed abnormal condition, parking abnormal condition, uncoupling abnormal condition, coupling abnormal condition, obstacle condition, multi-locomotive path conflict condition. The geographical conditions include track gradient, turning radius, etc. The weather conditions include rainy days, heavy snow, fog, day, night, etc. In addition, in addition to the above abnormal conditions, other abnormal conditions are also configured, such as microcomputer interlocking signal failure, communication failure, and the locomotive cannot start, etc.

[0046] The locomotive control module 150 is the test subject of the automatic driving algorithm. By configuring the automatic driving algorithm to be tested in the locomotive control module 150, the locomotive control module 150 adopts the automatic driving algorithm to be tested, and generates an automatic driving decision to control the locomotive according to at least one of the locomotive positioning information, locomotive train control information, locomotive perception data, locomotive dispatching instructions, and locomotive operating conditions, so as to realize the test of the automatic driving algorithm.

[0047] The perception module 120, the dispatching module 130, the working condition module 140, or the locomotive control module 150 can be integrated in the simulation module 110, or can be an external platform or external system independent of the simulation module 110, which is not limited here.

[0048] In this embodiment, the locomotive automatic driving simulation system performs integrated positioning simulation and locomotive operation simulation through the simulation module 110 to generate locomotive positioning information and locomotive train control information, configures locomotive perception data through the perception module 120, provides locomotive dispatching instructions through the dispatching module 130, and sets the locomotive operation conditions through the working condition module 140. It can simulate complex railway operation environments so that the locomotive control module 150 can test or verify the performance of the automatic driving algorithm under different operation environments, realizing the locomotive automatic driving simulation test. Moreover, this system has high flexibility and scalability, can adapt to different test requirements, and provides strong support for the development or optimization of locomotive automatic driving technology.

[0049] In an embodiment of the present application, the locomotive automatic driving simulation system further includes a display module for displaying at least one of locomotive positioning information, locomotive train control information, locomotive perception data, locomotive dispatching instructions, and locomotive operation conditions.

[0050] Configuring the locomotive perception data includes: constructing a virtual environment and a sensor model through the simulation module 110, and integrating the sensor model and the locomotive model into the virtual environment; simulating the driving scenario of the locomotive model in the virtual environment, and collecting data through the sensor model in the driving scenario to obtain locomotive perception data.

[0051] In this embodiment, the perception module 120 can be configured in the simulation module 110 to configure locomotive perception data through perception simulation. Among them, perception simulation includes environment simulation, sensor simulation, scenario simulation, and data generation. Environment simulation refers to constructing a virtual environment. Sensor simulation refers to simulating physical characteristics such as sensor resolution, field of view angle, and noise, as well as data output. Scenario simulation refers to designing different driving scenarios, such as normal driving, emergency obstacle avoidance, and bad weather. Data generation refers to generating sensor data, such as images, point clouds, radar signals, etc., for use by the perception algorithm. Schematically, the steps of perception simulation are as follows:

[0052] First, use simulation tools such as high-precision 3D modeling tools to construct a virtual locomotive driving environment, including railways, buildings, vegetation, vehicles, pedestrians, traffic signs, etc., and set environment parameters, such as weather, lighting, traffic flow, etc.

[0053] Secondly, build a sensor model and configure sensor parameters, such as camera resolution, lidar line count, radar field of view angle, etc., and integrate the sensor model into the virtual environment.

[0054] Then, different driving scenarios are simulated in a virtual environment, such as: a normal scenario where a locomotive is running normally on a railway, a dangerous scenario where a pedestrian suddenly crosses the road, the locomotive brakes suddenly or an obstacle appears, and extreme scenarios such as driving in bad weather conditions like heavy rain, thick fog, heavy snow, night driving, and driving under strong light.

[0055] Finally, data collection is carried out in the simulated driving scenarios through the sensor model to obtain locomotive perception data. For example, by simulating the camera imaging process through the sensor model, including resolution, frame rate, exposure, noise, etc., data such as RGB images and depth images are generated; by simulating the point cloud data of a lidar, including the number of scan lines, field of view angle, noise, etc., 3D point cloud data is generated; by simulating the reflected signal of a millimeter-wave radar, including distance, speed, angle, etc., radar point clouds or target lists are generated.

[0056] In this embodiment, the locomotive autonomous driving simulation system can achieve the following objectives through the perception module 120: <{

[0057] 1. Scenario verification: Simulate various locomotive driving scenarios in a virtual environment, such as railway tracks, bad weather, etc., and support dynamic scenarios, such as traffic flow, pedestrian behavior, weather changes, etc., to verify the robustness of perception.

[0058] 2. Sensor simulation: Simulate the data output of different sensors such as cameras, millimeter-wave radars, lidars, etc., to test the multi-sensor fusion ability of perception.

[0059] 3. Fault simulation: Simulate sensor faults or data loss to test the fault tolerance ability of perception.

[0060] In an embodiment of the present application, a map model and a locomotive model are built, and fusion positioning simulation is carried out according to the map model and the locomotive model to obtain locomotive positioning information, including: obtaining locomotive railway map information, building a map model based on the locomotive railway map information; building a locomotive model in the map model according to the physical characteristics and motion characteristics of the locomotive; simulating the position of the locomotive model in the map model according to the coordinates of the locomotive model and the coordinates of the map model to obtain initial positioning information, and obtaining locomotive positioning information based on the initial positioning information.

[0061] In some embodiments, according to the locomotive railway map information, a virtual map and tracks (railways) are established in simulation software to obtain a map model. For example, according to the characteristics of the railway network, graphic information and geometric information of railway lines in high-precision maps such as ArcGIS can be extracted as locomotive railway map information to build a map model. Schematically, the map model can be expressed as follows:

[0062] MMAP=(GIS,CAD,SIM,CO,GM) Equation (1)

[0063] Among them, MMAP is the map model, GIS is the high-precision map data collected in reality, CAD is the graphic information and geometric information extracted from the high-precision map, SIM is the virtual map, tracks, etc. built in the simulation software, CO is the GIS coordinate data and the simulation software coordinate data, and GM is the conversion method of the positioning coordinates, such as the Gaussian mapping algorithm for converting geodetic coordinates to longitude and latitude coordinates, etc.

[0064] In some embodiments, building a locomotive model includes locomotive physical object modeling and locomotive dynamic characteristic modeling. For locomotive physical object modeling, the creation method of the locomotive can be called through the model initialization code to update the data of the locomotive list and create the locomotive, and assign labels, icons, etc. to the corresponding locomotive. Please refer to Figure 2 , Figure 2 is the flowchart of locomotive physical object modeling shown in a specific embodiment of the present application. As Figure 2 shown, the locomotive physical object modeling process is as follows:

[0065] Initialize and call the code for creating a locomotive object, clear the locomotive list table and clear the locomotive object (locomotive model) in the locomotive buffer; define the locomotive capacity in the locomotive buffer, create a locomotive model, and record it in the locomotive list; read the locomotive name ID and record it on the locomotive Label (tag); combine the data and modify the locomotive icon to change it to the locomotive number corresponding to the locomotive.

[0066] For locomotive dynamic characteristic modeling, a virtual locomotive object can be established on the map model in the simulation software according to the physical structure characteristics and motion characteristics of the locomotive. First, establish a virtual physical model object of the locomotive according to the geometric dimensions and three-dimensional model of the real locomotive's length, width, and height. Then, combine the characteristics of the simulation software to establish a kinematic model of the locomotive as the locomotive model. Schematically, the locomotive model can be expressed as follows:

[0067] MDYN = (LO, AC, DE, MaS, MiS) Equation (2)

[0068] Among them, MDYN is the locomotive model, LO is the virtual locomotive object, AC is the locomotive acceleration, DE is the locomotive deceleration, MaS is the maximum speed of the locomotive, and MiS is the minimum speed of the locomotive. When the locomotive enters the acceleration or deceleration state during the locomotive operation simulation, its speed calculation method is as follows:

[0069] V(t) = V1 + at Equation (3)

[0070] Wherein, V(t) is the locomotive speed at time t, V1 is the current locomotive speed, t is time, and a is the general term for the locomotive acceleration AC and the locomotive deceleration DE. When the locomotive speed reaches the defined maximum locomotive speed MaS or the minimum locomotive speed MiS, the locomotive runs at a constant limit speed (the maximum locomotive speed MaS or the minimum locomotive speed MiS). It should be understood that the locomotive acceleration AC and the locomotive deceleration DE can be parametrically adjusted according to different actual locomotive characteristics, or changed under specific conditions. Similarly, the maximum locomotive speed MaS or the minimum locomotive speed MiS is the same.

[0071] In addition, based on MDYN, the driving mode of the locomotive power system can be further simulated. Combining parameters such as the motor traction force, braking force, and track surface friction force, the running and braking processes of the locomotive are simulated. Then, considering the locomotive loading situation, different dynamic state responses are carried out according to different numbers of attached carriages, different coupling methods, different weights of heavy objects, and different numbers of attachments. Through the locomotive acceleration and deceleration parameter tables under different loads, the locomotive acceleration AC and the locomotive deceleration DE are finally converted.

[0072] In some embodiments, the positioning of the locomotive model in the map model can be simulated according to the plane coordinates of the locomotive model and the longitude and latitude coordinates of the map model, as the initial positioning information. Please refer to Figure 3 , Figure 3 is the locomotive longitude and latitude calculation flowchart shown in a specific embodiment of the present application. As Figure 3 shown, the locomotive longitude and latitude calculation process is as follows:

[0073] First, obtain the x and y coordinates of the locomotive, that is, the plane coordinates, according to the locomotive data; sequentially define parameters such as the geodetic coordinates, the meridian value of the central meridian, the regional width, the equatorial radius, the earth's variability, and the angle to radian at the location of the locomotive in the map model; calculate the longitude and latitude of the locomotive according to the defined parameters and the x and y coordinates of the locomotive, calculate the geodetic coordinates of the locomotive according to the longitude and latitude calculation results, and output the results and print them on the locomotive object as the initial positioning information.

[0074] In some embodiments, the initial positioning information can be directly used as the locomotive positioning information, or the initial positioning information can be corrected to obtain the locomotive positioning information, which is not limited here.

[0075] In an embodiment of the present application, obtaining the locomotive positioning information based on the initial positioning information includes: obtaining locomotive perception data by interacting with the perception module 120; comprehensively obtaining the locomotive positioning information according to the locomotive perception data and the initial positioning information.

[0076] In this embodiment, based on the simulation of the locomotive's planar coordinates and longitude and latitude coordinates, when the locomotive reaches a specific position on the railway track, it is also necessary to simulate other sensor data as the locomotive's perception data to assist in the multi-dimensional positioning of the locomotive. Therefore, the simulation module 110 can obtain the locomotive's perception data through interaction with the perception module 120. Among them, if the perception module 120 is integrated in the simulation module 110, the simulation module 110 can directly interact with the perception module 120. If the perception module 120 is an external platform or external system independent of the simulation module 110, the interaction between the simulation module 110 and the perception module 120 can be achieved by building an interface module.

[0077] Please refer to Figure 4 , Figure 4 which is a schematic diagram of the laser distance positioning simulation shown in a specific embodiment of the present application. As Figure 4 shown, when the locomotive reaches the end line position, a laser sensor model can be configured on the locomotive model through the perception module 120 to simulate the laser emitting to the end line light baffle, obtain the distance feedback, and use the distance as the locomotive's perception data to form the locomotive positioning information with the initial positioning information. Please refer to Figure 5 , Figure 5 which is a schematic diagram of the QR code recognition positioning simulation shown in a specific embodiment of the present application. As Figure 5 shown, when the locomotive approaches coupling objects such as tank cars and flat cars, on the basis of the laser simulation, an image acquisition device model can also be configured on the locomotive model through the perception module 120 to simulate QR code recognition, obtain the QR code recognition result, and use the distance and the QR code recognition result as the locomotive's perception data to form the locomotive positioning information with the initial positioning information, so as to provide a coupling positioning basis for the subsequent locomotive model to perform the coupling task.

[0078] In this embodiment, based on the map model, according to the position of the locomotive model in the simulation software, the initial positioning information of the locomotive model is simulated, and the sensor model on the locomotive model is simulated through the perception module 120 to obtain the locomotive's perception data, realizing the simulation of complete locomotive positioning data and improving the simulation accuracy of locomotive positioning. During the simulation process, parameter adjustment and system optimization can also be carried out according to the actual situation to further improve the simulation accuracy of locomotive positioning.

[0079] In an embodiment of the present application, locomotive operation simulation is carried out through the locomotive model to obtain locomotive train control information, including: obtaining locomotive control instructions by interacting with the locomotive control module 150, where the locomotive control module 150 is also used to initiate locomotive control instructions; executing the locomotive control instructions through the locomotive model to simulate the locomotive operation state and obtain locomotive train control information.

[0080] In this embodiment, the locomotive control instruction is used to control the start, stop, acceleration, deceleration, horn sounding, sand spreading, etc. of the locomotive. The simulation module 110 can obtain the locomotive control instruction through interaction with the locomotive control module 150. Among them, if the locomotive control module 150 is integrated in the simulation module 110, the simulation module 110 can directly interact with the locomotive control module 150. If the locomotive control module 150 is an external platform or external system independent of the simulation module 110, the interaction between the simulation module 110 and the locomotive control module 150 can be realized by building an interface module.

[0081] In some embodiments, the locomotive control module 150 generates a locomotive control instruction based on a preset automatic driving algorithm according to the current locomotive perception data and sends it to the simulation module 110. After receiving the locomotive control instruction sent by the locomotive control module 150, the simulation module 110 performs locomotive operation simulation according to the locomotive control instruction through the locomotive model, generates the next train control information and returns it to the locomotive control module 150.

[0082] In an embodiment of the present application, the perception module 120 can be configured in the simulation module 110 to generate a locomotive dispatching instruction through dispatching simulation. Among them, the dispatching simulation includes software simulation and scenario simulation. Software simulation refers to implementing dispatching logic through dispatching algorithms, such as path planning, task allocation, traffic flow management, etc. Through path planning, the optimal path is planned for the locomotive, considering traffic conditions, road restrictions, task requirements, etc. Through task allocation, tasks (such as cargo transportation, etc.) are assigned to the locomotive to optimize resource utilization. Through traffic flow management, the locomotive driving is coordinated to avoid congestion and conflicts and improve the overall traffic efficiency; Scenario simulation refers to designing different traffic scenarios. Schematically, the steps of the dispatching simulation are as follows:

[0083] First, set the environmental parameters to form a virtual environment, such as work tasks, traffic flow density, signal light cycle, weather conditions, etc., and integrate the locomotive model into the virtual environment.

[0084] Then, implement dispatching logic, such as path planning, task allocation, traffic flow management, etc., and integrate the dispatching algorithm into the simulation module 110.

[0085] Finally, design different traffic scenarios, including normal scenarios, extreme scenarios and special scenarios. Among them, normal scenarios are such as the locomotive driving normally on the railway, extreme scenarios are such as emergencies such as traffic congestion and traffic accidents during peak hours, bad weather, etc., and special scenarios are such as priority passage for emergency locomotives, traffic management for large-scale events, etc.

[0086] In this embodiment, the locomotive automatic driving simulation system can achieve the following goals through dispatching simulation:

[0087] 1. Efficiency optimization: Optimize locomotive path planning, task allocation and traffic flow management to improve the overall efficiency.

[0088] 2. Scenario testing: Simulate complex traffic scenarios (such as congestion, accidents, special events, etc.) to test the robustness of scheduling.

[0089] 3. Resource management: Test the scheduling and management capabilities of resources such as locomotives, charging stations, maintenance workshops, etc.

[0090] In another embodiment of the present application, initiating a locomotive scheduling instruction includes: obtaining locomotive driving information by interacting with the locomotive control module 150, and obtaining carriage perception data by interacting with the perception module 120. Among them, the locomotive control module 150 is further configured to generate locomotive driving information, and the perception module 120 is further configured to configure carriage perception data; sending a microcomputer interlocking instruction to the simulation module 110 according to the locomotive driving information and the carriage perception data to obtain the microcomputer interlocking information fed back by the simulation module 110. Among them, the simulation module 110 is further configured to build a ground equipment model, and in response to the microcomputer interlocking instruction, simulate the operation of the ground equipment through the ground equipment model to generate microcomputer interlocking information; initiate a locomotive scheduling instruction according to the microcomputer interlocking information.

[0091] In this embodiment, if the scheduling module 130, the perception module 120, and the locomotive control module 150 are integrated in the simulation module 110, direct interaction can be carried out between the simulation module 110, the scheduling module 130, the perception module 120, and the locomotive control module 150. If the scheduling module 130, the perception module 120, and the locomotive control module 150 are external platforms or external systems independent of the simulation module 110, interaction between them can be achieved by building an interface module.

[0092] The locomotive control module 150 performs locomotive control based on the autonomous driving algorithm to be tested, in combination with at least one of locomotive positioning information, locomotive train control information, locomotive perception data, locomotive scheduling instructions, and locomotive operating conditions, and generates locomotive driving information. For example, the locomotive control module 150 can perform locomotive control based on the autonomous driving algorithm to be tested, according to the locomotive scheduling instruction initiated by the scheduling module 130 last time, generate the current locomotive driving information, and return it to the scheduling module 130.

[0093] The perception module 120 can also perform carriage perception simulation through the simulation module 110 to obtain carriage perception data and provide it to the scheduling module 130. Among them, the carriage refers to a non-powered freight carriage such as a tank car or a flat car that needs to be towed by a locomotive. The perception module 120 can build a sensor model of the carriage through the simulation module 110, integrate the carriage model and the sensor model of the carriage into the virtual environment, collect data through the sensor model of the carriage in the driving scenario to obtain carriage perception data. Among them, the simulation module 110 is further configured to build a carriage model, and the carriage model includes at least one of a tank car model, a flat car model, etc. Correspondingly, the carriage perception data includes at least one of tank car perception data, flat car perception data, etc. AsFigure 6 As shown, taking the construction of a tank truck model as an example, the physical object modeling process of the tank truck is as follows:

[0094] Initialize and call the code to create a tank truck object, clear the tank truck list table, and clear the tank truck object (tank truck model) in the tank truck buffer area; define the tank capacity in the tank truck buffer area, create a tank truck model, and record it in the tank truck list; read the tank truck name ID and record it on the tank truck label; define the tank truck length and deploy an iron bull (a molten iron tractor used to tow the tank truck to align under the blast furnace).

[0095] The dispatching module 130 combines the locomotive driving information and the car body perception data to generate a microcomputer interlocking instruction, and sends it to the simulation module 110, so that the simulation module 110 simulates the operation of the ground equipment to generate and return the microcomputer interlocking information, and then generates a locomotive dispatching instruction according to the microcomputer interlocking information and sends it to the locomotive control module 150. This process can also realize the test of the dispatching algorithm.

[0096] Schematically, the simulation of the ground equipment operation includes at least one of the simulation of the barrier machine operation, the simulation of the signal machine operation, the simulation of the turnout operation, etc.

[0097] In an embodiment of the present application, the simulation of the ground equipment operation is performed through the ground equipment model to generate the microcomputer interlocking information, including: performing the simulation of the barrier machine operation through the barrier machine model to generate the barrier machine status information, where the barrier machine status information includes the normal operation status information of the barrier machine or the fault status information of the barrier machine, and the ground equipment model includes the barrier machine model; performing the simulation of the signal machine operation through the signal machine model to generate the signal machine status information, where the signal machine status information includes the normal display status information of the signal machine or the abnormal status information of the signal machine, and the ground equipment model includes the signal machine model; performing the simulation of the turnout operation through the turnout model to generate the turnout status information, where the turnout status information includes the normal conversion status information of the turnout or the fault status information of the turnout, and the ground equipment model includes the turnout model; generating the microcomputer interlocking information according to at least one of the barrier machine status information, the signal machine status information, and the turnout status information.

[0098] The barrier machine, that is, the crossing barrier machine, is an important safety device at the railway crossing, used to lower the barrier when a train approaches, prevent vehicles and pedestrians from entering the crossing, and ensure the safety of train operation. The signal machine is a key device in the railway signal system, used to transmit operation instructions to the train driver. The turnout is a key device in the railway signal system, used to guide the train to turn from one track to another.

[0099] In this embodiment, the barrier machine simulation includes hardware simulation, software simulation, communication simulation, and environment simulation. Hardware simulation refers to simulating the physical devices of the barrier machine, such as motors, barrier poles, sensors, etc. Software simulation refers to simulating the control logic and action logic of the barrier machine. Communication simulation refers to simulating the communication between the barrier machine and the interlocking system (such as the dispatching module 130 in this application), such as serial ports, Ethernet, etc. Environment simulation refers to simulating the operating environment where the barrier machine is located, such as train approach, crossing status, etc. Schematically, the steps of the barrier machine simulation are as follows:

[0100] First, perform barrier machine modeling, including physical models, logical models, and communication models. For the physical model, establish the physical characteristic model of the barrier machine, including motor action time, barrier pole position, sensor status, etc. For the logical model, establish the control logic model of the barrier machine, including action rules (such as lowering, raising) and interlocking conditions. For the communication model, establish the communication protocol model between the barrier machine and the interlocking system.

[0101] Then, design different barrier machine simulation scenarios, including normal action scenarios and fault scenarios. Normal action scenarios include normal lowering actions (the barrier machine lowers when a train approaches) and normal raising actions (the barrier machine raises after a train passes). Fault scenarios include barrier machine motor failures, sensor failures, communication interruptions, etc.

[0102] The action logic of the barrier machine is usually determined by the interlocking system according to conditions such as train approach signals and crossing status. For example, lowering the barrier and raising the barrier. Lowering the barrier means that when a train approaches, the barrier machine lowers the barrier pole to prevent vehicles and pedestrians from entering the crossing. Raising the barrier means that after a train passes, the barrier machine raises the barrier pole to resume crossing traffic.

[0103] Common barrier machine faults include motor failures (such as the barrier machine being unable to lower or raise), sensor failures (such as the barrier machine being unable to detect the position of the barrier pole), communication interruptions (the barrier machine being unable to receive control instructions from the interlocking system). In the simulation, these faults can be simulated by modifying model parameters or injecting fault signals.

[0104] The signal machine simulation includes hardware simulation, software simulation, communication simulation, and environment simulation. Hardware simulation refers to simulating the physical devices of the signal machine, such as lamp positions, filaments, control circuits, etc. Software simulation refers to simulating the control logic and display logic of the signal machine. Communication simulation refers to simulating the communication between the signal machine and the interlocking system, such as serial ports, Ethernet, etc. Environment simulation refers to simulating the operating environment where the signal machine is located, such as train approach, track occupancy, etc. Schematically, the steps of the signal machine simulation are as follows:

[0105] First, signal modeling is carried out, including physical model, logical model and communication model. For the physical model, a physical characteristic model of the signal is established, including red light, green light, yellow light, etc. For the logical model, a control logic model of the signal is established, including display rules (such as red light, green light, yellow light, etc.) and interlocking conditions; for the communication model, a communication protocol model between the signal and the interlocking system is established.

[0106] Then, different signal simulation scenarios are designed, including normal train operation scenarios (such as when a train passes a signal and the signal shows a green light), shunting operation scenarios (such as when the signal shows a white light or a blue light), and fault scenarios (such as when the filament of the signal breaks and shows a red light or goes out).

[0107] The display logic of the signal is usually determined by the interlocking system according to conditions such as track occupancy, switch position, and route status. For example: when the route is clear, a green light is allowed to show for the train to pass; when the route is clear, but there is a speed limit ahead, a yellow light is shown; when the route is occupied or the train is prohibited from passing, a red light is shown.

[0108] Common signal failures include filament breakage (such as when the signal shows a light out or a red light), communication interruption (such as when the signal cannot receive control instructions from the interlocking system), and power failure (such as when the signal cannot work properly). In the simulation, these failures can be simulated by modifying model parameters or injecting fault signals.

[0109] Switch simulation includes hardware simulation, software simulation, communication simulation and environment simulation. Hardware simulation refers to simulating the physical devices of the switch, such as the point machine, indicating rod, etc. Software simulation refers to simulating the control logic and action logic of the switch. Communication simulation refers to simulating the communication between the switch and the interlocking system, such as serial port, Ethernet, etc. Environment simulation refers to simulating the operating environment where the switch is located, such as train approach, track occupancy, etc. Schematically, the steps of switch simulation are as follows:

[0110] First, switch modeling is carried out, including physical model, logical model and communication model. For the physical model, a physical characteristic model of the switch is established, including the action time of the point machine, the position of the indicating rod, etc.; for the logical model, a control logic model of the switch is established, including action rules (such as normal position, reverse position) and interlocking conditions. For the communication model, a communication protocol model between the switch and the interlocking system is established.

[0111] Then, different switch simulation scenarios are designed, including normal conversion scenarios and fault scenarios. Normal action scenarios include the switch converting from the normal position to the reverse position, or from the reverse position to the normal position. Fault scenarios include switch jamming, indication error, etc.

[0112] The action logic of the switch is usually determined by the interlocking system according to conditions such as route status, track occupancy, etc. For example: the normal position means the switch is in the straight track position; the reverse position means the switch is in the turnout track position.

[0113] Common turnout failures include jamming (e.g., the turnout cannot be switched to the proper position), indication errors (e.g., the actual position of the turnout does not match the indicated position), and communication interruptions (e.g., the turnout cannot receive control instructions from the interlocking system). In the simulation, these failures can be simulated by modifying the model parameters or injecting fault signals.

[0114] In this embodiment, the locomotive automatic driving simulation system can achieve the following objectives through the working simulation of the ground equipment model:

[0115] 1. Function verification: Verify whether the actions of the barrier machine, signal machine, and turnout are correct under different interlocking logics.

[0116] 2. Performance testing: Test the response speed and stability of the barrier machine, signal machine, and turnout in complex scenarios.

[0117] 3. Fault simulation: Simulate the failures of the barrier machine, signal machine, and turnout, and test the fault tolerance of the locomotive automatic driving simulation system.

[0118] In an embodiment of the present application, the working condition module 140 is further configured to configure locomotive PLC information; the working condition module 140 interacts with the simulation module 110 according to the locomotive PLC instruction to generate locomotive PLC information and feedback it to the locomotive control module 150, wherein the locomotive control module 150 is further configured to initiate the locomotive PLC instruction, and the simulation module 110 is further configured to respond to the locomotive PLC instruction and simulate the locomotive actions through the locomotive model.

[0119] In an embodiment of the present application, the working condition module 140 is further configured to configure car body PLC information; the working condition module 140 interacts with the simulation module 110 according to the car body PLC instruction to generate car body PLC information and feedback it to the dispatching module 130, wherein the dispatching module 130 is further configured to initiate the car body PLC instruction, and the simulation module 110 is further configured to build the car body model and simulate the car body actions through the car body model in response to the car body PLC instruction.

[0120] In this embodiment, the locomotive PLC instruction is mainly used to perform hook-on and hook-off operations on the locomotive. Correspondingly, the locomotive action simulation refers to simulating actions such as unhooking and hooking through the locomotive model. Schematically, after receiving the locomotive perception data, the locomotive control module 150 will release the locomotive PLC instruction; the car body PLC instruction is mainly used to unhook parked tank cars, flat cars, etc. Correspondingly, the car body action simulation refers to simulating actions such as parking and unhooking through the car body model. Schematically, after receiving the car body perception data, the dispatching module 130 will release the car body PLC instruction.

[0121] This embodiment performs PLC simulation through the interaction between the working condition module 140 and the simulation module 110 to achieve the control and simulation of the PLC of the underlying equipment.

[0122] In some embodiments of the present application, abnormal locomotive speed conditions include abnormal starting speed conditions, abnormal running speed conditions, and abnormal stopping speed conditions. Abnormal locomotive speed conditions refer to simulating abnormal locomotive speed situations during normal locomotive driving control. During the starting stage, abnormal simulations of the locomotive accelerating too slowly or too quickly can be performed. During the running process, abnormal simulations of gear states can be carried out. For example, the upper limit speed of gear 1 is 5 km / h, and the situation where the locomotive exceeds 5 km / h can be simulated; the upper limit speed of gear 2 is 10 km / h, and the situation where the locomotive exceeds 10 km / h can be simulated. During the locomotive stopping stage, abnormal simulations of the locomotive deceleration process can be performed. For example, when receiving braking force, abnormal simulations include the locomotive suddenly stopping with a speed of 0, or the locomotive decelerating very slowly and the speed remaining unchanged, or the locomotive speed exceeding the limit abnormally.

[0123] In some embodiments of the present application, for the simulation of abnormal parking conditions, for example: during the driving of a tanker truck, when the speed becomes 0, if a parking instruction is received, normally the tanker truck needs to execute the parking task and feedback the completion status. Therefore, the simulation of abnormal parking refers to simulating the situation of parking failure, that is, when a parking instruction is received, the signal of successful execution is not feedback.

[0124] In some embodiments of the present application, for the simulation of abnormal uncoupling conditions, for example: during the coupling process of a locomotive and a tanker truck, simulating the hook coupling device to achieve the coupling between the locomotive and the tanker truck. When the speeds of the locomotive and the tanker truck become 0, if an uncoupling instruction of the hook coupling device is received, normally the coupling devices on the locomotive and the tanker truck need to execute the hook lifting task, complete the uncoupling and feedback the completion status. Therefore, the simulation of abnormal uncoupling refers to simulating the situation of uncoupling failure, that is, when an uncoupling instruction is received, the hook lifting action of the hook coupling device is not executed, the coupled locomotive or tanker truck does not release the coupling relationship, and the signal of successful execution is not feedback.

[0125] In some embodiments of the present application, for the simulation of abnormal coupling conditions, for example: when the locomotive receives a coupling task, normally the locomotive will drive over to couple with the tanker truck. When approaching the tanker truck to be coupled, simulate the locking and coupling of the coupling device to achieve the coupling between the locomotive and the tanker truck, and the coupling between tanker trucks, and feedback the signal of successful coupling. Therefore, the simulation of abnormal coupling refers to simulating the situation of coupling failure, that is, when the locomotive or tanker truck approaches the tanker truck to be coupled (the distance from the tanker truck to be coupled is 0), the coupling task is not executed, that is, the command of Hitch is not executed, and the two colliding objects do not establish a coupling relationship, and the signal of successful coupling and the establishment of the coupling relationship are not externally feedback.

[0126] In some embodiments of the present application, the obstacle condition refers to simulating the appearance of obstacles for the locomotive during driving and feedbacking obstacle information. Such asFigure 7 As shown, obstacles can be simulated on the track where the locomotive travels, and obstacle information can be set, including the type of the simulated obstacle, the size of the obstacle, the distance of the obstacle, etc.

[0127] In some embodiments of the present application, for the multi-locomotive path conflict condition, the path conflict can be resolved by dividing the priority levels of tasks. Since there will be path conflicts when multiple locomotives are traveling, such as Figure 8 As shown, the task levels can be divided for the tasks, allowing a certain locomotive to complete the tasks of the first priority level first and occupy the corresponding track, and other locomotives need to wait in place, thereby resolving the path conflict.

[0128] In some embodiments of the present application, the geographical condition includes at least one of the track gradient, turning radius, etc. Since different track gradients and turning radii will affect the control of the locomotive's automatic driving and result in different speeds, different track gradients, turning radii, etc. can be set through geographical environment simulation, thereby testing different control strategies in the automatic driving algorithm.

[0129] In some embodiments of the present application, for the configuration of the weather condition, such as Figure 9 As shown, weather simulation can be performed through the simulation model, including sunny days, rainy days, snowy days, foggy days, etc., to achieve the configuration of the weather condition.

[0130] Please refer to Figure 10 , Figure 10 which is the architecture diagram of the locomotive automatic driving simulation system shown in a specific embodiment of the present application. As Figure 10 shown, the locomotive automatic driving simulation system includes a simulation platform, a perception part, a scheduling part, a scenario part, a platform part, and a BS interface product. Among them, the simulation platform is an example of the simulation module 110, the perception part is an example of the perception module 120, the scheduling part is an example of the scheduling module 130, the scenario part is an example of the working condition module 140, the platform part is an example of the locomotive control module 150, and the BS interface product is an example of the display module.

[0131] The perception part, scheduling part, scenario part, platform part, and BS interface product, as external systems, interact with the simulation platform through the interface module for data exchange. The main content of the interaction between the simulation platform and the perception part is perception simulation, including locomotive perception and tanker perception. By simulating the visual perception functions of locomotives and tankers, the coupling relationship between tankers and locomotives is judged to obtain locomotive perception data and tanker perception data. The perception part will transfer the received locomotive perception data to the platform part again so that the simulation platform can receive control instructions sent by the platform part based on the autonomous driving algorithm, such as locomotive control instructions and the feedback of the real-time state of the locomotive. The scheduling part will release microcomputer interlocking instructions to the simulation platform after receiving tanker perception data and locomotive driving information such as the speed and direction of the locomotive from the platform part, and obtain the microcomputer interlocking information feedback by the simulation platform. Among them, microcomputer interlocking data such as microcomputer interlocking instructions and microcomputer interlocking information are exchanged between the simulation platform and the scheduling part for the transmission and feedback of track control information. The platform part interacts with the scenario part after receiving locomotive perception data, releases device instructions such as locomotive PLC instructions, and releases train control instructions to the simulation platform, that is, locomotive control instructions, for the autonomous driving algorithm to send running and coupling tasks to the locomotive model in the simulation platform and for the locomotive to complete the corresponding tasks. After receiving the device instructions, the scenario part interacts with the simulation platform for device instructions and simulation. After receiving the path and task information released by the scheduling part, the BS interface product displays the corresponding information of tankers and locomotives on the interface.

[0132] In some embodiments, for the modeling of the interface module, TCP and UDP can be used for the communication management of the in-loop test, and the simulation platform exchanges data with the external system through these two methods.

[0133] In some embodiments, the microcomputer interlocking instructions are used to control signal lights, sections, and switches to achieve the transmission and feedback of track information. For example Figure 11 As shown, the turn-on control instruction is a specific example of the microcomputer interlocking instruction. First, the simulation platform receives the turn-on control instruction returned by the locomotive dispatching program in the scheduling part and judges whether the data is correct, including whether the message length of this instruction conforms to the rule and whether the message ID is the turn-on instruction sent by the locomotive dispatching program. After confirming that the data is correct, define the starting lamp pair and the opposing lamp from the array, match the starting lamp and the opposing lamp ID of the model, locate the lamp pair object, print out the message, call the microcomputer interlocking turn-on code, and perform the turn-on operation of the model. Among them, the turn-on is mainly achieved by matching the starting lamp and the opposing lamp ID with the lamp pair object of the model and then changing the icon color of it to turn on the signal light.

[0134] In some embodiments, the vehicle control data is interacted between the simulation platform, the dispatching part, and the platform part. The received and feedback data is used for the automatic driving algorithm to send control instructions and feedback the real-time status of the locomotive. The dispatching part gives information or instructions such as the driving path, start, and stop of the locomotive. The platform part actually controls the vehicle's driving according to the information or instructions of the dispatching part, and controls the vehicle according to the rotational speed feedback by the vehicle and the obstacle information given by the sensing part. The simulation platform receives the locomotive control instructions from the platform part, judges whether this instruction conforms to the rules, after confirming that the data is correct, matches the model locomotive number, and stores the data situation in the locomotive object attributes into the task number, then judges whether it is the last task, and from which end to couple the tank car, reads the number and information of the tank cars, as well as the coordinates of the task object and saves them into the locomotive attributes, reads the number and object information of the signal lamp pairs, reads the track where the locomotive is located and the target process point, reads the task type, and judges the driving direction of the locomotive through the coordinates of the signal machine.

[0135] In some embodiments, the perception data of the tank car / flat car is interacted between the simulation platform, the sensing part, and the dispatching part. The received and feedback data is used for the visual perception function of the simulated tank car / flat car to judge the coupling relationship between the tank car / flat car and the locomotive. As Figure 12 shown, the simulation platform creates an empty array, adds the tank car perception data into this array, including the tank car ID, the tank car coupling information, the QR code information, the data type, and the reserved bytes, and then obtains the corresponding socket of the tank car for data transmission.

[0136] In some embodiments, the locomotive perception data is interacted between the simulation platform, the sensing part, and the platform part. The received and feedback data is used for the train control simulation operation of the locomotive. As Figure 13 shown, the simulation platform loops through all the locomotives in the locomotive list, obtains the corresponding running locomotive object and ID, adds the locomotive perception data into the message, including the heartbeat, vehicle speed, standby vehicle speed, sand spreading, whistle status, digital input / output, brake output instruction, throttle position, diesel engine speed, brake cylinder air pressure, train pipe air pressure, fault code, main reservoir air pressure, working condition selection bit, locomotive voltage, main generator current, engine water temperature, lubricating oil temperature, lubricating oil pressure, remote control vehicle condition, transmission box oil temperature, smoke, flame detection warning, emergency stop, differential pressure, grounding, overcurrent and other data, and then obtains the corresponding interface socket through the corresponding locomotive ID for data transfer. In addition, as Figure 14 shown, when performing the locomotive perception simulation, obstacles can be set on the track where the locomotive is traveling, and the obstacle simulation data is collected through the sensor model to realize the perception of obstacle simulation.

[0137] In some embodiments, the PLC data is interacted between the simulation platform and the scenario part for controlling and simulating the underlying device PLC, including simulating the locomotive, tank car / flat car PLC. The simulation platform respectively obtains the device instructions of the locomotive PLC instructions and the tank car / flat car PLC instructions, and performs corresponding operations according to each device instruction. For example, Figure 15 As shown, the simulation platform receives the message of the locomotive PLC data, that is, the locomotive PLC instruction, parses the message, obtains the locomotive object, message ID, and control ID, determines whether to execute the instruction, and executes and prints the coupling and uncoupling instructions after confirming the execution. For example, Figure 16 As shown, the simulation platform receives the message of the tank car PLC data, that is, the tank car PLC instruction, parses the message, obtains the message ID, tank car ID, parking, uncoupling and other tank car control instructions, and operates according to the tank car control instructions.

[0138] The following will elaborate on the locomotive automatic driving simulation system in detail by simulating and analyzing the multi-locomotive path conflicts of the locomotive automatic driving simulation system.

[0139] In a real railway transportation system, path conflicts may occur when multiple locomotives are running. Under complex working conditions, this may lead to accidents or a decrease in operating efficiency. The locomotive automatic driving simulation system provided by the embodiments of the present application can be used to build a map model MMAP, place multiple locomotive models MDYN on multiple virtual tracks, simulate the path conflicts that may occur during the parallel operation of multi-vehicle tasks and the operation process, as well as simulate some working conditions of on-site operations, including complex working conditions such as abnormal locomotive speed, abnormal parking, abnormal uncoupling, abnormal coupling, obstacles on the road, communication interruption, and abnormal microcomputer interlocking system. The automatic driving algorithm is analyzed and evaluated using the simulation results. When building a multi-locomotive path conflict simulation experiment, the system can also combine various factors, such as the topological structure of the railway line, the running speed of the locomotive, and the path selection strategy, to ensure the accuracy and reliability of the simulation experiment. The system can also be used to evaluate and optimize the dispatching strategy of the railway transportation system, improving the operating efficiency and safety of the railway transportation system. Through the simulation methods of the map model, locomotive model, and cross-system interface, as well as the software-in-the-loop virtual commissioning method, the system builds a locomotive automatic driving simulation model, providing an effective platform and low-cost rehearsal ground for the functional verification, optimized dispatching, virtual commissioning, and software-in-the-loop testing of the automatic driving algorithm.

[0140] The present application also provides a locomotive automatic driving simulation method, which is applied to the locomotive automatic driving simulation system provided in each of the above embodiments. The method includes: respectively performing fusion positioning simulation and locomotive operation simulation through the simulation module 110 to obtain locomotive positioning information and locomotive train control information; configuring locomotive perception data through the perception module 120; initiating a locomotive dispatching instruction through the dispatching module 130; configuring the locomotive operation condition through the working condition module 140; and performing locomotive control based on a preset automatic driving algorithm and in combination with at least one of the locomotive positioning information, locomotive train control information, locomotive perception data, locomotive dispatching instruction, and locomotive operation condition through the locomotive control module 150 to complete the test of the automatic driving algorithm.

[0141] The locomotive automatic driving simulation method provided in the above embodiment and the locomotive automatic driving simulation system provided in the above embodiment belong to the same concept. The implementation processes of each step have been described in detail in the system embodiment, and will not be elaborated here.

[0142] The above embodiments only exemplarily illustrate the principle and its effects of the present application, rather than being used to limit the present application. Any person familiar with this technology can modify or change the above embodiments without departing from the spirit and scope of the present application. Therefore, all equivalent modifications or changes completed by those with ordinary knowledge in the technical field without departing from the spirit and technical idea disclosed by the present application should still be covered by the claims of the present application.

Claims

1. A locomotive automatic driving simulation system, characterized in that, The system includes: A simulation module, configured to build a map model and a locomotive model, perform integrated positioning simulation based on the map model and the locomotive model to obtain locomotive positioning information, and perform locomotive operation simulation through the locomotive model to obtain locomotive train control information; A perception module, configured to configure locomotive perception data; A dispatching module, configured to issue locomotive dispatching instructions; A working condition module, configured to configure locomotive operation working conditions, where the locomotive operation working conditions include at least one of a driving working condition, a geographical working condition, and a weather working condition; A locomotive control module, configured to perform locomotive control based on a preset automatic driving algorithm and in combination with at least one of the locomotive positioning information, the locomotive train control information, the locomotive perception data, the locomotive dispatching instructions, and the locomotive operation working conditions, and complete the test of the automatic driving algorithm.

2. The locomotive automatic driving simulation system according to claim 1, characterized in that Building a map model and a locomotive model, performing integrated positioning simulation based on the map model and the locomotive model to obtain locomotive positioning information, includes: Obtaining locomotive railway map information, and building the map model based on the locomotive railway map information; Building the locomotive model in the map model according to the physical characteristics and motion characteristics of the locomotive; Simulating the position of the locomotive model in the map model according to the coordinates of the locomotive model and the coordinates of the map model to obtain initial positioning information, and obtaining the locomotive positioning information based on the initial positioning information.

3. The locomotive automatic driving simulation system according to claim 2, wherein Obtaining the locomotive positioning information based on the initial positioning information, includes: Obtaining the locomotive perception data by interacting with the perception module; Comprehensively obtaining the locomotive positioning information according to the locomotive perception data and the initial positioning information.

4. The locomotive automatic driving simulation system according to claim 1, characterized in that, Performing locomotive operation simulation through the locomotive model to obtain locomotive train control information, includes: Obtaining a locomotive control instruction by interacting with the locomotive control module, where the locomotive control module is further configured to issue the locomotive control instruction; Executing the locomotive control instruction through the locomotive model, simulating the locomotive operation state, and obtaining the locomotive train control information.

5. The locomotive automatic driving simulation system according to claim 1, characterized in that, Configuring locomotive perception data, includes: Building a virtual environment and a sensor model through the simulation module, and integrating the sensor model and the locomotive model into the virtual environment; Simulating the driving scenario of the locomotive model in the virtual environment, and collecting data in the driving scenario through the sensor model to obtain the locomotive perception data.

6. The locomotive automatic driving simulation system according to claim 1, characterized in that, Issuing locomotive dispatching instructions, includes: Obtaining locomotive driving information by interacting with the locomotive control module, and obtaining car body perception data by interacting with the perception module, where the locomotive control module is further configured to generate the locomotive driving information, and the perception module is further configured to configure the car body perception data; Sending a microcomputer interlocking instruction to the simulation module according to the locomotive driving information and the car body perception data to obtain the microcomputer interlocking information fed back by the simulation module, where the simulation module is further configured to build a ground equipment model, and in response to the microcomputer interlocking instruction, perform ground equipment operation simulation through the ground equipment model to generate the microcomputer interlocking information; Issuing the locomotive dispatching instruction according to the microcomputer interlocking information.

7. The locomotive automatic driving simulation system according to claim 6, wherein Performing ground equipment operation simulation through the ground equipment model to generate the microcomputer interlocking information, including: Performing barrier machine operation simulation through the barrier machine model to generate barrier machine status information, where the barrier machine status information includes normal operation status information or fault status information of the barrier machine, and the ground equipment model includes the barrier machine model; Performing signal machine operation simulation through the signal machine model to generate signal machine status information, where the signal machine status information includes normal display status information or abnormal status information of the signal machine, and the ground equipment model includes the signal machine model; Performing turnout operation simulation through the turnout model to generate turnout status information, where the turnout status information includes normal conversion status information or fault status information of the turnout, and the ground equipment model includes the turnout model; Generating the microcomputer interlocking information based on at least one of the barrier machine status information, the signal machine status information, and the turnout status information.

8. The locomotive automatic driving simulation system according to claim 1, characterized in that The working condition module is further configured to configure the car body PLC information; The working condition module interacts with the simulation module according to the car body PLC instruction to generate the car body PLC information and feedback it to the dispatching module, where the dispatching module is further configured to initiate the car body PLC instruction, and the simulation module is further configured to build a car body model and perform car body action simulation through the car body model in response to the car body PLC instruction.

9. The locomotive automatic driving simulation system according to claim 8, characterized in that, The working condition module is further configured to configure the locomotive PLC information; The working condition module interacts with the simulation module according to the locomotive PLC instruction to generate the locomotive PLC information and feedback it to the locomotive control module, where the locomotive control module is further configured to initiate the locomotive PLC instruction, and the simulation module is further configured to perform locomotive action simulation through the locomotive model in response to the locomotive PLC instruction.

10. A locomotive automatic driving simulation method, applied to the locomotive automatic driving simulation system according to any one of claims 1-9, characterized in that, The method includes: Performing fusion positioning simulation and locomotive operation simulation respectively through the simulation module to obtain locomotive positioning information and locomotive train control information; Configuring locomotive perception data through the perception module; Initiating a locomotive dispatching instruction through the dispatching module; Configuring the locomotive operation working condition through the working condition module; Performing locomotive control through the locomotive control module based on a preset automatic driving algorithm and in combination with at least one of the locomotive positioning information, the locomotive train control information, the locomotive perception data, the locomotive dispatching instruction, and the locomotive operation working condition to complete the test of the automatic driving algorithm.

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