Train autonomous driving system and method using railway signal

Abstract

The present invention relates to a communication-based train control system using railway signals, and more specifically, to a communication-based train control system using railway signals that presents an all-encompassing innovative solution ranging from track infrastructure to control systems and operating systems, with autonomous driving technology and a data-based real-time train control system as core axes.

Description

Technology Field

[0001] The present invention relates to a communication-based train control system using railway signals, and more specifically, to a communication-based train control system using railway signals that presents an all-encompassing innovative solution ranging from track infrastructure to control systems and operating systems, by performing Automatic Train Protection (ATP) functions or simulating safe spacing and branching scenarios between virtually connected trains, with autonomous driving technology and a data-based real-time train control system as core axes. Background Technology

[0002] Railways play a pivotal role in modern society as a highly efficient means of transportation in terms of mass transport and punctuality. However, the existing railway system is currently revealing several limitations compared to the rapidly increasing demand for transport and the density of train operations.

[0003] First and foremost, the biggest problem with the existing railway system is its low level of automation and high reliance on human error. Since the operation and control of trains still rely heavily on human resources such as engineers and controllers, the risk of accidents caused by human error is ever-present. In fact, an analysis of past railway accidents reveals that a significant number stemmed from mistakes or errors in judgment by operational personnel.

[0004] Furthermore, relying on manual driving presents a problem where the quality and efficiency of operation depend on the engineer's skill level or physical condition. It is difficult to consistently maintain an optimal driving pattern, leading to reduced energy efficiency, and it is also difficult to optimize the control of train spacing or the adjustment of operating intervals.

[0005] The second problem is that decision-making for train operations remains concentrated in the hands of controllers. Since high-level decision-making, such as establishing train operation plans and responding to unexpected situations, is delegated to the control center, it increases the workload of controllers and sometimes leads to delays in decision-making.

[0006] It is nearly impossible for controllers to synthesize and make decisions based on the vast amount of information involved in train operations in real time, and consequently, they end up making decisions based on limited information. There is also a risk that missing the golden time in the event of an emergency could lead to a major accident. Furthermore, if a physical problem occurs at the control center, the entire train operation could be paralyzed, so it can be said that there is a significant risk at the system level.

[0007] The third problem is the lack of coordination between trains and inefficient operation methods. In the current railway system, trains operate independently, and there is almost no information sharing or cooperation between preceding and following trains. This is a major factor hindering the efficient utilization of track capacity.

[0008] If train spacing or speed profiles are managed only on an individual train basis, the operation of preceding and following trains cannot be coordinated, resulting in unnecessary acceleration and deceleration and wasted track capacity. On the other hand, if organic cooperation between trains is achieved, safety can be ensured while minimizing train spacing, thereby increasing transport efficiency.

[0009] The fourth problem is the insufficient utilization of real-time information and the absence of prediction-based operations. Vast amounts of operational data generated in real-time both inside and outside the train are not being collected or utilized, effectively going to waste. Despite various sensors being attached to onboard equipment and trackside facilities, the big data generated from them is currently not being reflected in train operations in real time.

[0010] By collecting and analyzing real-time status information of onboard equipment, track conditions, and the surrounding environment, it is possible to detect risk factors such as train breakdowns, track failures, and adverse weather conditions in advance and respond proactively. However, the utilization of such information is currently very limited.

[0011] Furthermore, the concept of predictive maintenance—which involves analyzing patterns from past operational history and accumulated data to predict problem occurrences and take preventive measures—has not been sufficiently implemented in railway systems. As a result, not only is effective risk prevention difficult, but there are also limitations in enhancing train availability and reliability.

[0012] Fifth, there is a lack of standard procedures and systems for a rapid response in the event of an emergency. As railway accidents can lead directly to massive casualties, the speed and effectiveness of emergency measures are critical. However, it is true that clear protocols for responding to emergencies have not been established, and related systems are inadequate.

[0013] Passive responses relying on the experience of controllers or train drivers inevitably lead to missing the golden time in crisis situations, and there is a problem in that the quality of the response depends on individual capabilities. Railway emergencies are not only diverse in type but often take on complex aspects, so there are limitations in preparing response manuals for each individual case.

[0014] As such, the current railway system has fundamental limitations, including low levels of automation and intelligence, insufficient utilization of real-time information, and a centralized decision-making structure. This leads to a decline in the safety and efficiency of railway operations, making it difficult to effectively respond to the rapidly increasing future demand for railway transportation.

[0015] Furthermore, amidst intensifying competition with other modes of transportation, more innovative changes are urgently needed to secure the competitiveness of the railway system. There is a need to build a safe, efficient, and sustainable future railway system through a paradigm shift across the entire system, moving beyond mere partial improvements.

[0016] The key solutions to fundamentally resolve these problems of the existing railway system and enable future-oriented railway operations lie, above all, in 'autonomous driving technology' and 'real-time information-based train control'.

[0017] First, applying autonomous driving technology to railways can eliminate human error and consistently achieve optimal driving. Autonomous driving systems can overcome the inefficiencies of manual driving by analyzing vast amounts of data in real time to independently derive the best driving patterns. Since autonomous trains perform optimal driving by comprehensively considering various performance indicators such as energy efficiency and ride comfort, they can significantly contribute to improving transportation efficiency.

[0018] Furthermore, autonomous driving technology enables the efficient utilization of human resources by reducing the workload of controllers and train drivers. By automating simple, repetitive tasks and decentralizing decision-making, personnel can be concentrated on high-value-added work. In particular, it is expected to be of great help in preventing accidents by enabling rapid response during emergencies.

[0019] Next, real-time data-based intelligent train control is crucial for enhancing operational efficiency and resilience. By coordinating the movement of all trains on the track based on real-time sensing information generated inside and outside the train and inter-train communication, track capacity can be maximized and energy efficiency improved.

[0020] For example, when a preceding and following train share driving information and engage in cooperative control, unnecessary deceleration or stopping can be minimized. By dynamically optimizing the safety distance between trains, track capacity can be increased, while energy consumption can be suppressed by reducing speed fluctuations at the individual train level. The effectiveness of integrated train control based on real-time information is expected to be particularly pronounced in high-density railway networks.

[0021] Furthermore, data-driven predictive control technology will play a significant role in proactive risk management. By detecting abnormal patterns from accumulated operational data and predicting potential problems, it is possible to respond preemptively to train breakdowns or track failures. Operational stability will be enhanced as risks can be minimized through methods such as performing maintenance and establishing avoidance routes before problems occur.

[0022] However, despite this potential, there are several challenges in integrating autonomous driving and real-time information-based control into railways. Above all, since the commercialization of autonomous driving is only possible if the high safety of train operations is guaranteed, securing system maturity and reliability will be the key factor.

[0023] Establishing the infrastructure and conditions necessary for autonomous driving—such as advancing core technologies like sensors, communication, and control, overhauling operating systems, and setting up relevant regulations and certification schemes—can be considered a prerequisite. This will entail long-term research and development, demonstration, and massive investment.

[0024] In addition, there are numerous technical issues that need to be resolved, such as ensuring safety in the transitional phase where autonomous trains and conventional manual trains coexist, switching to manual control in emergencies, and designing mutual cooperation mechanisms between autonomous trains. Response tasks at the institutional and social levels, such as changing the roles of operating personnel, reorganizing the organizational structure, and enhancing passenger capacity, are also formidable.

[0025] Nevertheless, efforts to overcome these challenges and implement a new railway operation paradigm must continue. This is because autonomous driving and real-time data-based train control will serve as the foundation leading the innovation of future railway systems.

[0026] Accordingly, the present invention aims to propose a new railway operating system that fundamentally resolves the problems of existing railway systems and realizes safe, efficient, and intelligent future train operations. The problem to be solved

[0027] The present invention was devised to improve upon the aforementioned problems and has the following objectives.

[0028] The objective of the present invention is to fundamentally resolve the problems arising from the low level of automation, reliance on human error, and inefficient operational structure of existing railway systems, and to establish a future-type train operating system with dramatically improved safety and efficiency.

[0029] More specifically, first, the purpose is to eliminate the inefficiency and accident risks associated with manual driving by automatically implementing optimal train operation through the integration of autonomous driving technology.

[0030] Second, by introducing a data-based real-time train control system, we aim to maximize the utilization of railway infrastructure and promote energy-efficient and safe train operations.

[0031] Third, by establishing a mechanism for real-time information sharing and cooperative control between trains, we realize the overall optimization of train operations and enable prediction-based active risk management.

[0032] Fourth, we intend to make every effort to prevent accidents and minimize damage by establishing standard protocols and systems capable of rapid response in emergencies.

[0033] Fifth, by providing an open platform for the advancement of autonomous driving technology and intelligent control systems, we aim to enable continuous technological innovation while contributing to the revitalization of the related industrial ecosystem.

[0034] Ultimately, the present invention aims to enhance competitiveness as a national key transportation system and establish a foundation for the sustainable growth of the future railway industry by innovating the safety, efficiency, and resilience of railway transportation. means of solving the problem

[0035] To achieve the above objectives, one embodiment of the present invention relates to a communication-based train control system using railway signals, wherein

[0036] Autonomous driving devices, railway systems, and monitoring and control systems are connected to a network, and

[0037] The above-mentioned autonomous driving device predicts and models train operation responses to various operating conditions and failure situations based on train current status information received from the above-mentioned railway system, and generates real-time driving data optimized for safe operation.

[0038] The above monitoring and control system is,

[0039] A bidirectional active coupling device that, based on real-time bidirectional communication between preceding and succeeding trains, transmits driving information of the preceding train to the succeeding train and actively controls the driving of the succeeding train based thereon, and simultaneously feeds back driving information of the succeeding train to the preceding train to synchronize the driving state of the preceding train with that of the succeeding train, thereby virtually connecting the preceding and succeeding trains into a single formation to automatically adjust the train spacing and maximize train operation efficiency; and

[0040] A simulation device that prevents train collisions by detecting potential collision risks in advance by monitoring the level of synchronization of driving information between the preceding and succeeding trains in real time while the preceding and succeeding trains are virtually connected by the above-mentioned bidirectional active connection device, and simulating safety gaps and branching scenarios between the virtually connected trains.

[0041] The above-mentioned bidirectional active connection device is,

[0042] Based on driving information received from the preceding train, driving control commands for the following train are generated and transmitted, and at the same time, driving information received from the following train is transmitted to the preceding train to synchronize the driving status of the preceding train with that of the following train.

[0043] The above railway system transmits actual train operation data to the above autonomous driving device to provide the current train status,

[0044] The above railway system includes trackside control equipment, an operation management system, and a communication network, and

[0045] The above-mentioned trackside control equipment consists of field equipment that directly controls train operations, including a track condition monitoring device, a signal, and a track switch.

[0046] The above-mentioned operation management system is a system that oversees overall railway operations, including functions for establishing train operation plans, managing schedules, and providing passenger information.

[0047] The above communication network is responsible for data communication between the above trackside control equipment and the above operation management system, and

[0048] The above railway system provides basic information necessary for autonomous driving by transmitting train operation data, track status information, and operation plans collected in real time to the above autonomous driving device, and

[0049] The above railway system implements an organic connection between the autonomous train and the existing railway infrastructure by transmitting and executing train control commands received from the autonomous driving device to the corresponding trackside control equipment.

[0050] The above monitoring and control system comprehensively analyzes data received from the autonomous driving device and the railway system to generate real-time monitoring and control commands for the autonomous train,

[0051] The above monitoring and control system includes a monitoring unit, a control unit, and a communication unit, and

[0053] The above monitoring unit comprehensively analyzes data received from the above autonomous driving device and the above railway system to monitor the train operation status in real time, and

[0054] The above control unit generates necessary train control commands based on monitoring results and transmits them to the autonomous driving device, and

[0055] The communication unit is responsible for transmitting and receiving data with the autonomous driving device and the railway system, and

[0056] The above monitoring and control system can directly intervene in train operation and perform manual control when necessary, and

[0057] It promotes the safe operation of trains, including measures such as forcibly stopping the train, changing the emergency route, and switching to manual mode in the event of an emergency, and

[0058] When manual control by the above monitoring and control system is initiated, the driving command generation function of the above autonomous driving device is temporarily suspended, and the above railway system controls train operation in accordance with the control of the above monitoring and control system, and

[0059] When the emergency situation is lifted and train operations are normalized, the monitoring and control system terminates manual control and returns to autonomous driving mode by resuming the driving command generation function of the autonomous driving device.

[0060] The above simulation device outputs an emergency braking signal to prevent collision between trains when the integrity between virtually connected trains is compromised and the safe gap between trains narrows below a set range or when the virtual connection is released, wherein

[0061] The above simulation device utilizes driving data of preceding and succeeding trains to monitor the level of synchronization of driving information between trains in real time, and

[0062] By collecting driving data including the speed, acceleration, and position of the preceding and following trains and comparing and analyzing it, the synchronization error between trains is calculated, and

[0063] The above simulation device comprehensively analyzes relative distance and speed information between trains along with the level of synchronization of driving information to predict and determine potential collision risk situations, and

[0064] The above simulation device predicts the possibility of a collision between trains at a future point in time by modeling changes in the relative distance and relative speed between trains based on real-time driving data of preceding and succeeding trains, and

[0065] If it is determined that the safe distance between trains will be violated after a certain period of time if the current driving pattern continues, the simulation device defines this as a potential collision risk situation, and

[0066] The above simulation device determines the integrity of the virtual connection based on the driving information synchronization level and the collision risk analysis results,

[0067] If the level of synchronization of driving information between trains is significantly low or the risk of collision exceeds a threshold, the simulation device considers this as the integrity of the virtual connection being compromised, and

[0068] When the above simulation device detects an exceptional situation in which the integrity of the virtual connection is compromised or the collision risk exceeds a threshold, it performs an active response by directly intervening in train control to issue deceleration or stopping commands to preceding and following trains, while applying emergency braking if necessary.

[0069] The present invention relates to a method for controlling an autonomous train in a communication-based train control system using railway signals, wherein

[0070] (a) A step in which an autonomous driving device receives train current status information from a railway system,

[0071] (b) A step in which the autonomous driving device predicts and models the train operation response to various operating conditions and failure situations based on the received train current status information and driving data collected through its own sensors,

[0072] (c) A step in which the autonomous driving device generates real-time driving data optimized for the safe operation of the train based on the prediction modeling results,

[0073] (d) A bidirectional active linkage device of the monitoring and control system transmits driving information of the preceding train to the following train through real-time bidirectional communication between the preceding and following trains, and feeds back driving information of the following train to the preceding train to synchronize the driving status of the preceding and following trains,

[0074] (e) A step in which a simulation device of the above-mentioned monitoring and control system monitors the level of synchronization of driving information of preceding and following trains in real time to determine the integrity between trains, and simulates safe gaps and branching scenarios between virtually connected trains to detect potential collision risks in advance, thereby preventing train collisions; is included.

[0075] The real-time driving data generated by the autonomous driving device in step (c) above functions as a type of optimal control guideline that encompasses the entire process of train operation,

[0076] By having all components of the train operate organically according to the aforementioned driving data, safe and efficient autonomous driving is realized, and

[0077] In the above-mentioned real-time driving data generation process, in order to ensure the real-time nature of the driving data, the autonomous driving device is implemented based on a high-performance embedded system, the data processing and computation processes are optimized, and the stability and low latency of the communication infrastructure between the train and the ground must be ensured.

[0078] The above real-time driving data incorporates fault detection and diagnosis functions and exception handling algorithms of the autonomous driving device to ensure the safe operation of the train even in unpredictable situations, and

[0079] To ensure that the aforementioned real-time driving data is verifiable and explainable, the algorithms used to generate the driving data and the sources of the data are documented, relevant information may be disclosed externally if necessary, and procedures and systems are established to periodically check and guarantee the consistency and validity of the driving data.

[0080] In order to ensure effective operation of the bidirectional active coupling device in step (d) above, simulation and verification of multiple scenarios are performed during the algorithm development process so that the driving control algorithm of the bidirectional active coupling device can faithfully reflect the actual train operation environment, and continuous algorithm optimization is performed using actual train operation data.

[0081] In order to maximize the performance of the simulation device in step (e) above,

[0082] To ensure the accuracy and reliability of the above simulation, the train behavior model and risk assessment model are processed in real time based on actual train operation data or by continuously optimizing pre-verified data.

[0083] In a communication-based train control system using railway signals, an autonomous driving device, a railway system, and a monitoring and control system are connected via a network; the autonomous driving device predicts and models train operation responses to various operating conditions and failure situations based on train current status information received from the railway system, thereby generating real-time driving data for safe operation; and the monitoring and control system is a bidirectional active coupling device that, based on real-time bidirectional communication between preceding and succeeding trains, transmits driving information of the preceding train to the succeeding train and actively controls the driving of the succeeding train based thereon, and simultaneously feeds back driving information of the succeeding train to the preceding train to synchronize the driving status of the preceding train with that of the succeeding train, thereby virtually connecting the preceding and succeeding trains as a single formation to automatically adjust the train spacing and maximize train operation efficiency. The system includes a simulation device that, while the preceding and succeeding trains are virtually connected by the bidirectional active connection device, monitors the level of synchronization of driving information between the preceding and succeeding trains in real time to determine the integrity between the virtually connected trains, performs independent signal control and Automatic Train Protection (ATP) functions based on database information storing the monitored data, or simulates safety gaps and branching scenarios between the virtually connected trains to detect potential collision risks in advance, thereby preventing train collisions; wherein the bidirectional active connection device generates and transmits driving control commands for the succeeding train based on driving information received from the preceding train, and simultaneously transmits driving information received from the succeeding train to the preceding train to synchronize the driving status of the preceding train with that of the succeeding train, and the railway system transmits actual train operation data to the autonomous driving device to provide the current train status, wherein the railway system includes trackside control equipment, an operation management system, and a communication network, and the trackside control equipment is composed of field equipment that directly controls train operation, including a track condition monitoring device, a signal, and a track switch.The above monitoring and control system comprehensively analyzes data received from the autonomous driving device and the railway system to generate real-time monitoring and control commands for the autonomous driving device, wherein the monitoring and control system includes a monitoring unit, a control unit, and a communication unit, wherein the monitoring unit comprehensively analyzes data received from the autonomous driving device and the railway system to monitor the train operation status in real time, the control unit generates specific train control commands based on the monitoring results and transmits them to the autonomous driving device, and the communication unit is responsible for data transmission and reception between the autonomous driving device and the railway system, and the monitoring and control system may directly intervene in train operation to perform manual control, and promotes the safe operation of the train by including measures such as forcibly stopping the train, changing the emergency route, or switching to manual mode in the event of an emergency, and when manual control by the monitoring and control system is initiated, the driving command generation function of the autonomous driving device is temporarily suspended, and the railway system controls train operation according to the control of the monitoring and control system, and when the emergency situation is resolved and train operation is normalized, the monitoring and control system terminates manual control and resumes the driving command generation function of the autonomous driving device to return to autonomous driving mode, and the simulation device [is] a train when the integrity between virtually connected trains is compromised When the safety gap between trains narrows below a set range or when the virtual connection is released, an emergency braking signal is output to prevent collision between trains, wherein the simulation device monitors the level of synchronization of driving information between trains in real time by utilizing driving data of preceding and following trains, and calculates the synchronization error between trains by collecting driving data including the speed, acceleration, and position of the preceding and following trains and comparing and analyzing it, and the simulation device predicts and determines a potential collision risk index by analyzing the relative distance and speed information between trains along with the level of synchronization of driving information.The above simulation device is characterized by predicting the possibility of a collision between trains at a future point in time by modeling the changes in relative distance and relative speed between trains based on real-time driving data of preceding and succeeding trains, in a communication-based train control system using railway signals.

[0084] The autonomous driving device (110) comprehensively analyzes the track status, signal information, and preceding train location data received from the railway system (120) and the train operation data collected through its own sensors to determine the real-time operation situation. Then, the autonomous driving device (110) derives the operation path, speed, and acceleration / deceleration pattern and generates a train control command accordingly. Furthermore, the autonomous driving device (110) utilizes predictive modeling technology to identify multiple operation conditions and potential risk factors in advance and prepare countermeasures. In this way, the autonomous driving device (110) simulates the train operation response for multiple scenarios based on accumulated operation data and machine learning algorithms.

[0085] The above-mentioned autonomous driving device (110) assumes situations such as track changes, signal failures, and adverse weather conditions, and predicts and determines the route, speed control, and braking pattern for the safe operation of the train in each situation, diagnoses in advance the risk of collision with a preceding train and the possibility of disaster situations such as fire and flooding, and establishes an emergency response system that can avoid risk factors or minimize damage. Effects of the invention

[0087] According to one embodiment of the present invention, the communication-based train control system using railway signals and the control method thereof according to the present invention have the following effects.

[0088] First, by organically linking autonomous driving devices, railway systems, and monitoring and control systems to collect, analyze, and utilize information necessary for autonomous train operation in real time, the safety and efficiency of train operation can be dramatically improved.

[0089] Second, by predicting and modeling various operating conditions and failure situations based on the train's current status information received from the railway system to generate optimal driving data, the autonomous driving device can detect potential risk factors in advance and respond effectively during train operation.

[0090] Third, the bidirectional active connection device of the monitoring and control system can maximize operational efficiency by automatically adjusting the train spacing through real-time bidirectional communication between preceding and following trains to synchronize the driving status of the trains and establish a virtual connection.

[0091] Fourth, the simulation device of the monitoring and control system can prevent train collision accidents by monitoring the integrity between virtually connected trains in real time and detecting potential collision risks in advance.

[0092] Fifth, by synchronizing driving information and actively controlling preceding and succeeding trains, autonomy and flexibility in train operations are secured, while enabling a rapid response in the event of an emergency, thereby enhancing the stability and resilience of the entire railway system.

[0093] Sixth, by providing a communication-based train control system capable of effectively linking and integrating with existing railway infrastructure, the present invention can promote the advancement and smartening of railway operations and ultimately contribute to strengthening the competitiveness of railway transportation.

[0094] Seventh, the autonomous train control method of the present invention can contribute to accelerating the practical application and commercialization of autonomous train operation technology by systematizing and optimizing the information flow and control procedures between each component. Brief explanation of the drawing

[0095] FIG. 1 is a block diagram illustrating the overall configuration of a communication-based train control system using railway signals according to the present invention. FIG. 2 is a conceptual diagram illustrating the interaction and information flow between an autonomous driving device, a railway system, and a monitoring and control system in a communication-based train control system using railway signals according to the present invention. FIG. 3 is a flowchart illustrating the virtual connection and spacing control process of preceding and succeeding trains by a bidirectional active connection device of a monitoring and control system according to the present invention. FIG. 4 is a flowchart illustrating the process of monitoring integrity between virtual connected trains and detecting collision risks by a simulation device of a monitoring and control system according to the present invention. FIG. 5 is a flowchart illustrating the process of predicting driving conditions and failure situations for generating real-time driving data of an autonomous driving device according to the present invention. FIG. 6 is an overall flowchart illustrating the operation process of a communication-based train control system using railway signals according to the present invention. FIG. 7 is a flowchart illustrating the detailed steps of a communication-based train control method using railway signals according to the present invention. FIG. 8 is a conceptual diagram illustrating an example of an operation scenario for an autonomous train applying a communication-based train control system and method using railway signals according to the present invention. Specific details for implementing the invention

[0096] The present invention as described above will be explained in detail through the attached drawings and embodiments.

[0097] It should be noted that the technical terms used in this invention are used merely to describe specific embodiments and are not intended to limit the invention. Furthermore, unless specifically defined otherwise in this invention, the technical terms used in this invention should be interpreted in the sense generally understood by those skilled in the art to which this invention pertains, and should not be interpreted in an overly broad or overly narrow sense. Additionally, if a technical term used in this invention is an incorrect technical term that fails to accurately express the concept of the invention, it should be replaced with a technical term that can be correctly understood by those skilled in the art. Moreover, general terms used in this invention should be interpreted according to their prior definitions or the context, and should not be interpreted in an overly narrow sense.

[0098] Furthermore, singular expressions used in the present invention include plural expressions unless the context clearly indicates otherwise. In the present invention, terms such as "composed of" or "comprising" should not be interpreted as necessarily including all of the various components or steps described in the invention, and should be interpreted as meaning that some of the components or steps may not be included, or that additional components or steps may be included.

[0099] Hereinafter, preferred embodiments according to the present invention will be described in detail with reference to the attached drawings. Identical or similar components are given the same reference number regardless of the drawing symbols, and redundant descriptions thereof will be omitted.

[0100] Furthermore, in describing the present invention, detailed descriptions of related prior art are omitted if it is determined that such descriptions could obscure the essence of the invention. Additionally, it should be noted that the attached drawings are intended only to facilitate an understanding of the concept of the present invention and should not be interpreted as limiting the concept of the present invention.

[0102] As illustrated in FIG. 1, the communication-based train control system (100) using railway signals according to the present invention is configured such that an autonomous driving device (110), a railway system (120), and a monitoring and control system (130) are connected to a network (140).

[0103] The autonomous driving device (110) is a core component installed on the train responsible for the train's autonomous driving, and generates real-time driving data optimized for safe operation by predicting and modeling the train's operation response to various operating conditions and failure situations based on the train's current status information received from the railway system (120).

[0104] Specifically, the autonomous driving device (110) includes a sensor unit (111), a communication unit (112), a control unit (113), a data processing unit (114), etc. The sensor unit (111) collects operational data, such as the train's position, speed, acceleration, and direction of travel, in real time from various sensors installed on the train. The communication unit (112) is responsible for transmitting and receiving data with the railway system (120) and the monitoring and control system (130). The control unit (113) controls the overall operation of the autonomous driving device (110), analyzes the collected data to establish a train operation plan, and generates optimal driving commands. The data processing unit (114) fuses and processes the received train status information and the collected sensor data to generate driving data.

[0105] The autonomous driving device (110) comprehensively analyzes data such as track status, signal information, and the location of a preceding train received from the railway system (120), and train operation data collected through its own sensors, to determine the real-time operation situation. Based on this, the autonomous driving device (110) derives the optimal driving route, speed, and acceleration / deceleration pattern, and generates train control commands accordingly to realize safe and efficient autonomous operation of the train.

[0106] In particular, the autonomous driving device (110) utilizes predictive modeling technology to identify various operating conditions and potential risk factors in advance and prepare countermeasures. To this end, the autonomous driving device (110) simulates train operation responses for various scenarios based on accumulated operation data and machine learning algorithms and derives an optimal response strategy.

[0107] For example, the autonomous driving device (110) assumes situations such as track changes, signal failures, and adverse weather conditions, and predicts and determines the optimal route, speed control, and braking pattern for the safe operation of the train in each situation. In addition, it pre-diagnoses the risk of collision with a preceding train and the possibility of disaster situations such as fire and flooding, and establishes an emergency response system that can avoid risk factors or minimize damage.

[0108] The real-time driving data generated through this predictive modeling is not only directly utilized for the safe operation of the train, but also provides useful information for establishing and optimizing railway operation plans. Furthermore, the autonomous driving device (110) performs continuous self-learning based on the accumulated driving data, thereby increasing prediction accuracy and improving real-time response capabilities.

[0109] Meanwhile, the railway system (120) is linked with existing railway infrastructure and operating systems to provide actual train operation data to the autonomous driving device (110). The railway system (120) includes trackside control equipment (121), an operation management system (122), a communication network (123), etc.

[0110] The trackside control equipment (121) consists of field equipment that directly controls train operations, such as track condition monitoring devices, signals, and track switches. The operation management system (122) is a system that oversees overall railway operations, including establishing train operation plans, managing schedules, and providing passenger information. The communication network (123) is responsible for data communication between the trackside control equipment (121) and the operation management system (122).

[0111] The railway system (120) provides basic information necessary for autonomous driving by transmitting train operation data, track status information, and operation plans collected in real time to the autonomous driving device (110). In addition, the railway system (120) implements an organic connection between the autonomous train and the existing railway infrastructure by transmitting and executing train control commands received from the autonomous driving device (110) to the corresponding trackside control equipment (121).

[0112] The monitoring and control system (130) is a high-level control system that encompasses the autonomous driving device (110) and the railway system (120), and performs the role of monitoring the overall train operation status in real time and issuing appropriate control commands when necessary. The monitoring and control system (130) is an essential component for the safe operation of the train and efficient traffic flow management.

[0113] The monitoring and control system (130) is largely composed of a monitoring unit (131), a control unit (132), and a communication unit (133). The monitoring unit (131) comprehensively analyzes data received from the autonomous driving device (110) and the railway system (120) to monitor the train operation status in real time. The control unit (132) generates necessary train control commands based on the monitoring results and transmits them to the autonomous driving device (110). The communication unit (133) is responsible for data transmission and reception with the autonomous driving device (110) and the railway system (120).

[0114] One of the key features of the present invention is that the monitoring and control system (130) includes a bidirectional active linking device (134) that enables real-time information exchange and cooperative control between preceding and succeeding trains, and a simulation device (135) that ensures safety between virtual linked trains.

[0115] The bidirectional active connection device (134) transmits driving information of the preceding train to the following train based on real-time bidirectional communication between the preceding and following trains and actively controls the driving of the following train based on this information, and at the same time feeds back the driving information of the following train to the preceding train to synchronize the driving state of the preceding train with that of the following train, thereby performing the function of virtually connecting the preceding and following trains into one trainset to automatically adjust the train spacing and maximize train operation efficiency.

[0116] Specifically, the bidirectional active linkage device (134) analyzes driving information, such as position, speed, and acceleration, received from the preceding train to calculate the optimal train spacing, and based on this, generates and transmits driving control commands for the following train (e.g., acceleration / deceleration commands, braking commands, etc.). At the same time, the driving information of the following train is also fed back to the preceding train, thereby enabling the preceding train to recognize the status of the following train in real time and synchronize the driving status.

[0117] Through such bidirectional information exchange and active control, train spacing can be minimized while maintaining a constant safe distance, thereby increasing track capacity and improving transport efficiency. Furthermore, the sharing of information between trains and close coordination enable a rapid response to unexpected situations, offering significant advantages in terms of safety.

[0118] Meanwhile, the simulation device (135) is responsible for verifying the driving safety between the preceding and succeeding trains virtually connected by the bidirectional active connection device (134) and diagnosing potential risk factors in advance.

[0119] The simulation device (135) comprehensively analyzes the driving information of preceding and following trains and track condition information, etc., and simulates the train behavior in a virtual connection state in real time. In particular, the simulation device (135) monitors the level of synchronization of driving information of preceding and following trains in real time to determine whether connection integrity is compromised due to delay or interruption in information exchange between trains, and detects potential collision risks in advance.

[0120] For example, independent signal control and Automatic Train Protection (ATP) functions may be performed based on the aforementioned monitored database information.

[0121] In addition, the simulation device (135) precisely models the interaction between trains for various operation scenarios (e.g., track merging, branching, turning around, etc.) and optimizes the safety distance between trains and the operation pattern based on the results. This enables train scheduling and route setting that can maximize track utilization while ensuring the stability of train operations.

[0122] If a risk of collision between virtually connected trains is detected as a result of the simulation, or if the safe distance between trains narrows below a certain level, the simulation device (135) immediately issues a warning message and simultaneously issues train control commands (e.g., forced deceleration, emergency braking, etc.) to prevent the risk of an accident. In addition, if necessary, the virtual connection is released and individual trains are controlled to operate independently, thereby allowing for flexible response to emergency situations.

[0123] In this way, by organically collaborating with the bidirectional active connection device (134) and the simulation device (135), train operation based on virtual connection can be implemented more safely and efficiently. Furthermore, as real-time information exchange and cooperative control are achieved between the autonomous driving device (110), the railway system (120), and the monitoring and control system (130) centered on these core components, the smartification and optimization of the entire railway transportation system can be promoted.

[0124] As illustrated in FIG. 2, the interaction and information flow between the autonomous driving device (110), the railway system (120), and the monitoring and control system (130) in the communication-based train control system (100) using railway signals according to the present invention are as follows.

[0125] First, the railway system (120) transmits actual train operation data collected from various sensors and control equipment installed at the site to the autonomous driving device (110) to provide current train status information. Here, the train operation data includes not only information directly related to train operation, such as the train's location, speed, acceleration, and direction of travel, but also comprehensive information regarding the train operation environment, such as track conditions, signal information, and operation schedule.

[0126] Specifically, the trackside control equipment (121) of the railway system (120) generates track status and train location information based on data collected through various sensors and detectors installed on the track and transmits it to the autonomous driving device (110) via a communication network (123).

[0127] For example, information on the occupancy status of each section of the track by the train, identified through a train detection device such as a track circuit and a track circuit, track condition information collected from a track condition monitoring device that detects the temperature and deformation of the track, and train direction information collected from a signal that controls the entry and exit of the train are processed by a trackside control facility (121) and provided to an autonomous driving device (110).

[0128] In addition, the operation management system (122) generates and transmits to the autonomous driving device (110) all information necessary for train operation, such as train operation plans, schedules, and train formation information. The operation management system (122) is a system that oversees the overall train operation, such as monitoring the train operation status in real time and adjusting the operation plan when necessary, and provides basic information necessary for the autonomous driving device (110) to establish an optimal operation strategy.

[0129] Meanwhile, the autonomous driving device (110) comprehensively analyzes train operation data received from the railway system (120) and driving data collected through its own sensor unit (111) to determine the real-time operation situation and generates an optimal driving command.

[0130] The data processing unit (114) of the autonomous driving device (110) fuses and analyzes in real time data such as track status, signal information, and operation schedule received from the railway system (120), and driving data such as the train's location, speed, and acceleration / deceleration collected through the on-board sensor unit (111). Through this, the data processing unit (114) precisely diagnoses the train's current operating status based on an integrated understanding of the environment surrounding the train.

[0131] Additionally, the control unit (113) of the autonomous driving device (110) generates an optimal driving command for safe and efficient train operation based on the analysis results of the data processing unit (114). The control unit (113) calculates the optimal train speed, acceleration / deceleration pattern, braking timing, etc., by considering the current train operation status and track conditions, and generates a driving command to control the train's propulsion and braking system according to the result.

[0132] The driving command generated in this way is transmitted to the railway system (120) and the monitoring and control system (130) through the communication unit (112) of the autonomous driving device (110). The trackside control equipment (121) of the railway system (120) controls the actual driving of the train according to the driving command received from the autonomous driving device (110).

[0133] For example, an acceleration command generated by the autonomous driving device (110) is converted into a signal that controls the output of the propulsion system and transmitted to the propulsion control device of the trackside control facility (121), and a braking command is converted into an operation signal of the braking system and transmitted to the braking control device. Through this, the driving command of the autonomous driving device (110) is realized as the physical movement of the actual train.

[0134] Meanwhile, the autonomous driving device (110) verifies the reliability of the driving data received from the railway system (120) in real time during the process of generating driving commands. If an error in specific data or a decrease in reliability is detected, the autonomous driving device (110) takes action by excluding the data from generating driving commands or minimizing its impact. This prevents the degradation of autonomous driving performance caused by the uncertainty of external data.

[0135] In addition, the autonomous driving device (110) continuously analyzes the correlation between the driving data received from the railway system (120) and the driving data generated by itself, and optimizes the driving command generation algorithm based on the results. Through this, the autonomous driving device (110) can promote effective information fusion with the railway system (120) and increase adaptability to the actual driving environment.

[0136] The monitoring and control system (130) monitors the interaction between the autonomous driving device (110) and the railway system (120) in real time and performs appropriate intervention when necessary. The monitoring and control system (130) receives driving command information from the autonomous driving device (110) and actual train operation status information from the railway system (120), and by comparing and analyzing the two, comprehensively evaluates and monitors the autonomous driving performance.

[0137] Specifically, the monitoring and control system (130) checks whether there is a match between the driving command generated by the autonomous driving device (110) and the actual train behavior accordingly, and analyzes the cause if a significant deviation occurs between the two. For example, if the actual train speed increase is minimal despite the acceleration command of the autonomous driving device (110), the monitoring and control system (130) comprehensively reviews factors affecting train operation, such as deterioration of track conditions or failure of the propulsion system, and establishes a countermeasure.

[0138] Additionally, the monitoring and control system (130) receives information regarding the safety and efficiency of train operation through the communication unit (112) of the autonomous driving device (110) and evaluates the performance of the autonomous driving algorithm based on this information. For example, the monitoring and control system (130) analyzes the risk of collision between trains predicted by the autonomous driving device (110), the results of calculating the optimal driving path, etc., to determine the prediction accuracy of the autonomous driving algorithm and the appropriateness of the decision-making.

[0139] If it is determined that the driving command of the autonomous driving device (110) is inappropriate or the performance of the autonomous driving algorithm is insufficient, the monitoring and control system (130) transmits an improvement command to the autonomous driving device (110). The improvement command may include modifying the driving command generation logic, strengthening the filtering of input data, adjusting autonomous driving algorithm parameters, etc., and the autonomous driving device (110) continuously improves its autonomous driving performance by updating related functions in accordance with the improvement command.

[0140] Furthermore, the monitoring and control system (130) can directly intervene in the operation of the train and perform manual control when necessary. This is mainly done to ensure the safe operation of the train in the event of an emergency, and specifically includes measures such as forcibly stopping the train, changing the emergency route, and switching to manual mode.

[0141] When manual control by the monitoring and control system (130) is initiated, the driving command generation function of the autonomous driving device (110) is temporarily suspended, and the railway system (120) controls the operation of the train according to the control of the monitoring and control system (130). Subsequently, when the emergency situation is resolved and the train operation is normalized, the monitoring and control system (130) terminates the manual control and resumes the driving command generation function of the autonomous driving device (110), thereby returning to the autonomous driving mode.

[0142] Meanwhile, the monitoring and control system (130) constantly monitors the communication quality between the autonomous driving device (110) and the railway system (120) and performs a rapid response in the event of a communication delay or failure. This is because autonomous driving technology relies heavily on real-time information exchange, so a deterioration in communication quality can have a direct impact on autonomous driving performance.

[0143] Accordingly, the monitoring and control system (130) tracks key communication quality indicators, such as data transmission delay time and packet loss rate between the autonomous driving device (110) and the railway system (120), in real time, and issues a warning message if a preset threshold is exceeded. At the same time, the monitoring and control system (130) performs network diagnosis to identify the cause of communication failures and, based on the results, immediately implements necessary measures, such as resetting the communication module and setting an alternative route, thereby ensuring the continuity of information exchange required for autonomous driving.

[0144] In addition, the monitoring and control system (130) accumulates and analyzes vast amounts of train operation data collected from the autonomous driving device (110) and the railway system (120) to optimize the autonomous driving system from a long-term perspective. By applying various data mining techniques, such as anomaly detection, fault prediction, and driving pattern analysis based on the accumulated data, factors that impede the safety and efficiency of autonomous driving are identified in advance, and strategies are established to respond appropriately to them.

[0145] As illustrated in FIG. 3, the bidirectional active connection device (134) of the monitoring and control system (130) according to the present invention is a core module that implements optimal train operation through real-time information exchange and cooperative control between preceding and succeeding trains.

[0146] The bidirectional active connection device (134) transmits driving information of the preceding train to the following train in real time and actively controls the driving of the following train based on this, thereby maintaining a safe distance between the preceding and following trains while maximizing the efficiency of train operation.

[0147] More specifically, the operation process of the bidirectional active connection device (134) is as follows.

[0148] First, the autonomous driving device (110) of the preceding train transmits driving data (e.g., position, speed, acceleration, etc.) collected through its own sensor unit (111) to a bidirectional active connection device (134) in real time (S301).

[0149] The bidirectional active connection device (134) analyzes driving data received from the preceding train to determine the current driving status of the preceding train (S302). At this time, the bidirectional active connection device (134) verifies the integrity of the received data and, if necessary, performs data correction or filtering to ensure data quality.

[0150] Next, the bidirectional active coupling device (134) derives the optimal driving pattern of the following train by comprehensively considering all information related to train operation, such as track information and operation schedule, along with the driving data of the preceding train (S303).

[0151] Specifically, the bidirectional active coupling device (134) finds the optimal balance point between the minimum gap (hereinafter 'safety gap') for maintaining a safe gap between the preceding train and the following train, and the minimum gap (hereinafter 'efficiency gap') for maximizing train operation efficiency. To this end, the bidirectional active coupling device (134) utilizes an optimization algorithm that considers various variables such as the driving speed and acceleration / deceleration pattern of the preceding train, the gradient and curvature of the track, and the braking performance of the train.

[0152] When the bidirectional active connection device (134) determines the optimal driving pattern of the trailing train, it generates a driving control command accordingly and transmits it to the driving device (110) of the trailing train (S304). The driving control command includes the target speed, acceleration / deceleration speed, and braking timing of the trailing train, and is used as reference information for controlling the propulsion and braking systems of the trailing train.

[0153] The autonomous driving device (110) of the trailing train controls the driving of the train according to a driving control command received from the bidirectional active connection device (134) (S305). As a result, while maintaining a safe distance from the preceding train, the train spacing is minimized, thereby maximizing track utilization.

[0154] Meanwhile, the bidirectional active connection device (134) also collects driving status information of the trailing train in real time (S306). The autonomous driving device (110) of the trailing train feeds back information such as the speed and position of the train that has changed as a result of executing a driving control command to the bidirectional active connection device (134).

[0155] The bidirectional active coupling device (134) comprehensively analyzes the driving information of the preceding train and the following train to determine the distance between trains and the relative speed (S307). Through this, it is possible to determine whether the current train distance is maintained within a safe distance and whether the relative speed between trains is within an allowable range.

[0156] If the train spacing violates safety standards or the relative speed is excessive, the bidirectional active coupling device (134) intervenes in train control according to a predefined emergency protocol (S308). For example, the bidirectional active coupling device (134) can prevent the risk of collision by simultaneously issuing deceleration or stop commands to the preceding train and the following train.

[0157] Under normal driving conditions, the bidirectional active connection device (134) continuously updates the optimal driving pattern using real-time driving data of the preceding and succeeding trains and transmits driving control commands accordingly to the succeeding train (S309). This series of processes is repeated until the train operation is terminated.

[0158] Consequently, the bidirectional active linkage device (134) dynamically adjusts the distance between trains by maintaining the preceding and succeeding trains in a kind of virtual connection state. This has the effect of simultaneously enhancing the safety and efficiency of train operation.

[0159] Another key function of the bidirectional active linkage device (134) is to transmit the driving information of the trailing train to the leading train to synchronize the driving status of the leading train with that of the trailing train. This enables mutually adaptive driving between trains.

[0160] Specifically, the bidirectional active connection device (134) analyzes driving information received from the trailing train and determines whether the actual driving state of the trailing train corresponds to the optimal driving pattern (S310).

[0161] If the actual running of the trailing train deviates from the optimal pattern (e.g., the gap between the trailing train and the preceding train widens or narrows excessively), the bidirectional active coupling device (134) transmits the information to the preceding train (S311).

[0162] The autonomous driving device (110) of the preceding train receives this information and adaptively adjusts its driving state (S312). For example, the preceding train may take measures such as temporarily decelerating to maintain a distance from the following train, or conversely, accelerating to actively widen the gap between trains.

[0163] In other words, the operation of the preceding train is dynamically optimized to match the state of the following train, thereby maximizing the effectiveness of coordinated operation between trains. This bidirectional data exchange and mutually adaptive driving control enable the preceding and following trains to operate organically as if they were a single train set.

[0164] Through the above process, the bidirectional active linkage device (134) supports the safe and efficient operation of multiple trains in a virtual connection state. Furthermore, this technology presents a new paradigm for the railway transportation system by introducing the concept of cooperative driving between trains.

[0165] Meanwhile, there are several technical considerations for the effective operation of the bidirectional active connection device (134).

[0166] First, the bidirectional active connection device (134) requires real-time data exchange between preceding and succeeding trains. To achieve this, a communication infrastructure between trains must be stably established, and sufficient bandwidth and low latency must be guaranteed. In particular, the performance and reliability of the communication system must be ensured so that smooth data exchange can be achieved in high-speed driving situations.

[0167] Next, the bidirectional active connection device (134) must process vast amounts of data related to train operation in real time. This requires high-performance computing resources and big data processing technology. In addition, a series of processes such as the collection, storage, and analysis of driving data must be automated and optimized to minimize system overhead.

[0168] In addition, the driving control algorithm of the bidirectional active linkage device (134) must be able to faithfully reflect the actual train operation environment. To this end, simulation and verification of various scenarios must be performed during the development process of the algorithm, and furthermore, continuous algorithm optimization using actual train operation data is necessary.

[0169] Finally, as the bidirectional active coupling device (134) is a core module of train control, its safety and reliability are of the utmost importance. To this end, a multi-safety design must be applied in both hardware and software, and regular safety inspections and maintenance must be performed. Furthermore, it is essential to provide a rear security device to prepare for emergency situations.

[0170] Considering all these factors, the present invention proposes a bidirectional active coupling device (134) as a core technology that enables safe and efficient train operation. Through the introduction of the bidirectional active coupling device (134), real-time information sharing and cooperative driving control between trains become possible, thereby allowing for the smartification of the entire railway transportation system.

[0171] Furthermore, the bidirectional active connection device (134) of the present invention is a foundational technology that can dramatically improve the operational efficiency of an autonomous driving system, and it is expected that it can be applied to various unmanned transport means beyond the railway sector. In the long term, it is believed that by enabling linkage and cooperative operation between multiple autonomous driving systems, it can present a new vision for future transportation systems.

[0172] As illustrated in FIG. 4, the simulation device (135) of the monitoring and control system (130) according to the present invention performs the function of verifying the driving safety between trains virtually connected by a bidirectional active connection device (134) and diagnosing potential risk factors in advance to prevent collisions.

[0173] Ensuring the safety of train operations is one of the most fundamental and important goals of a railway system. In particular, since the risk of collision between trains exists in situations where multiple trains are operating in close proximity, preemptive measures are essential. In response to this need, the simulation device (135) provides a solution that diagnoses and verifies the driving safety of a virtual connected train in real time.

[0174] The main functions of the simulation device (135) can be broadly divided into three categories. First is real-time monitoring of the level of synchronization of driving information between virtual connected trains, second is the prediction and judgment of the risk of collision between trains, and third is a preventive response to potential accident situations.

[0175] First, the simulation device (135) monitors the level of synchronization of driving information between trains in real time using driving data of preceding and succeeding trains (S401). Driving information synchronization refers to the degree to which trains in a virtual connection state accurately recognize changes in each other's driving states.

[0176] Specifically, the simulation device (135) collects driving data such as speed, acceleration, and position of the preceding train and the following train, and calculates the synchronization error between the trains by comparing and analyzing the data. If there is a discrepancy of more than a certain level in the driving state of the preceding and following trains, this indicates that there is a problem with information synchronization.

[0177] Synchronizing driving information between trains can be considered a necessary condition for safe operation. This is because maintaining an appropriate distance and preventing collisions are only possible if preceding and following trains accurately understand each other's driving status.

[0178] Therefore, the simulation device (135) uses the level of driving information synchronization as a key indicator to gauge the safety of the virtual connection train.

[0179] Next, the simulation device (135) comprehensively analyzes the relative distance and speed information between trains along with the driving information synchronization level to predict and determine a potential collision risk situation (S402).

[0180] To this end, the simulation device (135) models the changes over time in the relative distance and relative speed between trains based on real-time driving data of preceding and succeeding trains. In this process, various factors affecting train operation, such as the braking performance of the train, track conditions, and weather conditions, are considered as variables.

[0181] The simulation device (135) uses the train behavior model derived in this way to predict the possibility of a collision between trains at a future point in time. If it is determined that the safe distance between trains will be violated after a certain period of time if the current driving pattern continues, the simulation device (135) defines this as a potential collision risk situation.

[0182] Furthermore, the simulation device (135) applies a risk assessment model to quantify the severity of the predicted collision risk situation.

[0183] Factors such as the magnitude of the relative speed between trains, the remaining time until the expected collision point, track gradient, and curvature are considered in the risk assessment. The simulation device (135) classifies and manages collision risk situations in stages based on the risk index calculated through this multi-faceted analysis.

[0184] Meanwhile, the simulation device (135) determines the integrity of the virtual connection based on the driving information synchronization level and the collision risk analysis results (S403). Here, the integrity of the virtual connection means that multiple trains are connected in a state where safety and efficiency are ensured.

[0185] If the level of synchronization of driving information between preceding and succeeding trains is significantly low, or if the risk of collision exceeds a threshold, the simulation device (135) considers this to be a compromise of the integrity of the virtual connection. This implies that it is difficult to maintain the current virtual connection state as is.

[0186] Conversely, if the level of driving information synchronization is good and the risk of collision is also kept low, the simulation device (135) determines that the integrity of the virtual connection is secured. In this case, the train operation can be controlled in a way that maximizes driving efficiency while continuously maintaining the virtual connection between trains.

[0187] Since the determination of the integrity of the virtual connection is directly related to the safety of train operation, the simulation device (135) takes this into top priority. The conditions under which the virtual connection can be maintained are limited only to cases where safety is ensured, and even if there is room to improve operational efficiency, the principle is to disconnect the virtual connection unless safety is guaranteed.

[0188] As such, the simulation device (135) provides key information necessary for the safe operation of a virtual connected train through real-time analysis of driving data and preemptive prediction of train behavior. Furthermore, the simulation device (135) also performs the role of establishing and executing preventive response strategies for potential dangerous situations based on this information.

[0189] Specifically, the simulation device (135) directly intervenes in train control when an exceptional situation is detected, such as when the integrity of the virtual connection is compromised or the risk of collision exceeds a threshold (S404). The method and level of intervention are differentiated according to the urgency of the situation.

[0190] First, the simulation device (135) begins its response by issuing a deceleration or stopping command to the preceding and following trains. This is to prioritize securing a safe distance between trains, even at the cost of some reduction in operational efficiency. The intensity and duration of the deceleration and stopping commands are determined in proportion to the degree of impairment of the integrity of the virtual connection and the level of collision risk.

[0191] In some cases, more active interventions, such as applying emergency brakes to preceding and following trains, may be taken. This is generally a measure chosen in very dangerous situations where a collision is imminent. When emergency brakes are applied, an emergency announcement is simultaneously broadcast inside the train to ensure passenger safety.

[0192] In addition, the simulation device (135) can more effectively prevent collisions by issuing different control commands to the preceding and following trains. For example, in a situation where a collision is expected, it is a method of actively increasing the distance between trains by issuing an acceleration command to the preceding train and a deceleration or stop command to the following train.

[0193] Meanwhile, as a more fundamental countermeasure, the simulation device (135) may also disable the virtual connection and control the trains to operate independently (S405). This is typically selected when it is determined that the integrity of the virtual connection is severely compromised and that it is practically impossible to restore it.

[0194] When the virtual connection is disconnected, the simulation device (135) transmits relevant information to the autonomous driving device (110) of the trains. Based on this information, the autonomous driving device (110) derives an optimal driving pattern for independent driving, and accordingly receives control of the train and performs individual driving.

[0195] Through this series of processes, the simulation device (135) prevents the risk of collision of the virtual connecting train in advance through preemptive and multifaceted intervention. This directly leads to an improvement in the safety of train operations.

[0196] In short, the simulation device (135) serves as a last line of defense to ensure the safe operation of the virtual connecting train. Through the introduction of the simulation device (135), it has become possible to secure safety at the level of individual trains even in situations where multiple trains are operating in close proximity.

[0197] Furthermore, the simulation device (135) enhances the safety of train operations and ultimately contributes to increasing track capacity and improving transport efficiency. This is because if the integrity of the virtual connection is verified through simulation, the distance between trains can be narrowed, thereby increasing track utilization.

[0198] As such, the simulation device (135) of the present invention provides an innovative technological solution capable of achieving both safety and efficiency. The present invention is expected to open a new horizon for next-generation train control systems through the organic combination of the simulation device (135) and the previously introduced bidirectional active connection device (134).

[0199] Meanwhile, there are several prerequisites for maximizing the performance of the simulation device (135). First, the accuracy and reliability of the simulation must be guaranteed. To this end, the train behavior model and the risk assessment model must be continuously verified and optimized based on actual train operation data.

[0200] As illustrated in FIG. 5, the process of predicting driving conditions and failure situations for generating real-time driving data of the autonomous driving device (110) according to the present invention is largely composed of three stages.

[0201] The first step is for the autonomous driving device (110) to receive train current status information from the railway system (120) (S501). Train current status information is a concept that encompasses real-time train operation data and track environment data, and provides basic information necessary for train autonomous driving.

[0202] Specifically, the railway system (120) transmits track environment data, such as track gradient, curvature, and track switch status, along with operation data such as the train's current position, speed, and acceleration, to the autonomous driving device (110). In addition, operation planning data, such as train operation schedules and train formation information, is also provided.

[0203] The autonomous driving device (110) receives such data through the communication unit (112) and verifies the validity and integrity of the data. If an error or missing data is found in the received data, the autonomous driving device (110) performs a response such as requesting retransmission from the railway system (120) or estimating the operating status based on previously received data (S501).

[0204] The second step is to predict and model the train operation response to various operating conditions and failure situations based on the train current status information received by the autonomous driving device (110) and the driving data collected through the self-sensor unit (111) (S502).

[0205] First, the data processing unit (114) of the autonomous driving device (110) fuses the data received from the railway system (120) and the data collected from the sensor unit (111) to perform an integrated analysis of the train's current operating status. In this process, uncertainty and noise in the data are removed, and the correlation between operating status variables is identified.

[0206] Next, the control unit (113) of the autonomous driving device (110) generates various operation scenarios based on the analysis results. The operation scenarios assume various situations that the train may encounter during actual operation and cover a wide spectrum ranging from normal operation conditions to various failures and emergency situations.

[0207] For example, operational scenarios may include situations requiring operational restrictions due to track signal failures, vehicle system defects, or adverse weather conditions, situations requiring the maintenance of a minimum safe distance from a preceding train, and emergency response situations involving train derailment or the detection of track obstacles.

[0208] The autonomous driving device (110) predicts and models the optimal operating response of the train for each generated operating scenario. To this end, the autonomous driving device (110) utilizes various mathematical models for the train and track systems, such as a train dynamics model, a propulsion / braking system model, and a track interface model.

[0209] For example, under specific operating scenarios, the train's optimal speed profile, acceleration / deceleration patterns, and braking timing are derived through model-based simulations. In this process, key performance indicators such as train safety, ride comfort, and energy efficiency are comprehensively considered.

[0210] In addition, the autonomous driving device (110) evaluates the probability of occurrence and potential impact of each driving scenario. To this end, probabilistic safety evaluation techniques, failure tree analysis techniques, etc. are utilized, and past accident cases and driving history data are used as the basis for risk evaluation.

[0211] Through this series of processes, the autonomous driving device (110) can predict the optimal driving response of the train to various driving conditions and obstacle situations, and by reflecting this in the driving algorithm, it can maximize the safety and efficiency of the train's autonomous driving.

[0212] The third step is for the autonomous driving device (110) to generate real-time driving data optimized for safe operation based on the results of the predictive modeling (S503). The real-time driving data is a type of design variable used by the autonomous driving device (110) to control the actual operation of the train, and includes target values ​​for key elements of train operation.

[0213] Specifically, real-time driving data consists of variables that define the operation of the train propulsion / braking system, such as target speed, acceleration, braking force, and traction, as well as route control-related variables, such as the train's direction of travel, track switching points, and stopping stations. Furthermore, control variables for the energy-efficient operation of internal train facilities, such as heating and cooling systems and lighting systems, may also be included.

[0214] The control unit (113) of the autonomous driving device (110) synthesizes the results of the optimal driving response modeling for each driving scenario and derives real-time driving data that is most suitable for the current driving conditions. To this end, the control unit (113) utilizes advanced artificial intelligence technologies such as optimization algorithms and reinforcement learning.

[0215] For example, the control unit (113) can train a deep neural network model using simulation results for various driving scenarios as training data. The trained model outputs optimal driving data based on real-time input of the train's current status information.

[0216] In addition, the control unit (113) receives feedback on the actual driving results of the train and continuously updates the driving data generation model. Through this, the prediction accuracy and field applicability of the model are gradually improved, and furthermore, the overall performance of the autonomous driving device (110) is enhanced.

[0217] In this way, real-time driving data generated by the autonomous driving device (110) functions as a type of optimal control guideline that encompasses the entire process of train operation. All components of the train operate organically according to this guideline, thereby enabling safe and efficient autonomous driving.

[0218] Meanwhile, there are several important considerations in the process of generating real-time driving data. First, the real-time nature of the driving data must be guaranteed. Since train operating conditions change from moment to moment, the safety and efficiency of autonomous driving will inevitably be compromised if driving data is not rapidly updated accordingly.

[0219] To this end, the autonomous driving device (110) must be implemented based on a high-performance embedded system, and optimization of data processing and computation processes is required. In addition, ensuring the stability and low latency of the communication infrastructure between the train and the ground can also be considered a key prerequisite for generating real-time driving data.

[0220] Next, the driving data must be able to ensure the safe operation of the train even in unpredictable situations. Even if a sudden situation occurs during operation, the autonomous driving device (110) must be able to quickly recognize it and reflect an appropriate response strategy in the driving data.

[0221] To achieve this, the fault detection and diagnosis functions of the autonomous driving device (110), as well as the algorithms for handling exceptional situations, must be advanced. Furthermore, various safety regulatory conditions and operational constraints need to be systematically reflected in the process of generating driving data.

[0222] Finally, driving data must be verifiable and explainable. This is because the transparency and traceability of the driving data generation process must be guaranteed to enhance social acceptance of autonomous driving technology.

[0223] To this end, the autonomous driving device (110) must clearly document the algorithm used to generate driving data and the source of the data, and be able to disclose the relevant information externally when necessary. In addition, procedures and systems for periodically checking and guaranteeing the consistency and validity of the driving data must also be established.

[0224] As illustrated in FIG. 6, the operation process of the communication-based train control system (100) using railway signals according to the present invention is broadly divided into three stages. Each stage is achieved through organic interaction between the autonomous driving device (110), the railway system (120), and the monitoring and control system (130).

[0225] The first stage is an initialization stage, in which, when power is applied to the system, the components of the autonomous driving device (110), the railway system (120), and the monitoring and control system (130) each perform self-diagnosis and a process of establishing a communication channel between them proceeds (S601).

[0226] In the case of the autonomous driving device (110), the functions of major modules such as the sensor unit (111), communication unit (112), control unit (113), and data processing unit (114) are checked. Calibration of the sensor, setting of parameters of the control algorithm, and initialization of the communication protocol are performed at this stage.

[0227] The railway system (120) also diagnoses the status of trackside control equipment (121), operation management system (122), communication network (123), etc., and checks the readiness for operation. Tasks such as initial setup of track equipment, loading of operation plans and schedules, and verification of database connection are performed.

[0228] In the case of the monitoring and control system (130), the monitoring unit (131), the control unit (132), the communication unit (133), and the core components, the bidirectional active connection device (134) and the simulation device (135), are initialized. The operation of various monitoring tools, the loading of control algorithms, and the initialization of the user interface are performed.

[0229] In addition, at this stage, communication channels are established and sessions are managed between the autonomous driving device (110), the railway system (120), and the monitoring and control system (130). Each system performs mutual authentication according to a predefined communication protocol and prepares for data exchange through an encrypted channel.

[0230] When the initialization phase is completed, the autonomous driving device (110), the railway system (120), and the monitoring and control system (130) exchange status information with each other to verify the normal operation of the entire system. If a defect in a specific component or an abnormality in the communication channel is detected, an emergency response procedure is immediately initiated.

[0231] The second stage is a key stage in which real-time information exchange and cooperative control are performed between the autonomous driving device (110), the railway system (120), and the monitoring and control system (130) during train operation (S602).

[0232] First, the railway system (120) transmits real-time train operation data collected through the trackside control equipment (121) and the operation management system (122) to the autonomous driving device (110). This includes driving information such as the train's current position, speed, and acceleration, as well as track environment information such as the track gradient, curvature, and track switch status.

[0233] The autonomous driving device (110) fuses and analyzes the data received from the railway system (120) and the driving data collected through its own sensor unit (111) to determine the current operating status of the train in real time. Based on this, the autonomous driving device (110) determines the optimal train control command, namely the target speed, acceleration / deceleration speed, braking point, etc., and transmits this to the propulsion / braking system to implement autonomous driving of the train.

[0234] Meanwhile, the autonomous driving device (110) reports potential risk factors and constraints predicted during the process of generating train control commands to the monitoring and control system (130) in real time. This includes information such as the possibility of collision with a preceding train, detection of track obstacles, and detection of abnormal signs.

[0235] The monitoring and control system (130) analyzes information received from the autonomous driving device (110) to comprehensively evaluate the safety and efficiency of the train operation. If it is determined that there is a risk factor in the train operation, the monitoring and control system (130) sends an appropriate warning message to the autonomous driving device (110) or, if necessary, directly intervenes in the train control.

[0236] This series of processes is enhanced through the operation of a bidirectional active connection device (134) and a simulation device (135) within the monitoring and control system (130).

[0237] The bidirectional active connection device (134) dynamically controls the train spacing through real-time information exchange between the preceding and succeeding trains. The driving information of the preceding train is transmitted to the succeeding train to maintain a safe distance between the trains, while the driving status of the succeeding train is also fed back to the preceding train so that the driving of the preceding and succeeding trains is mutually adaptive.

[0238] The simulation device (135) verifies the driving safety between the virtually connected trains in real time.

[0239] It continuously monitors the operating patterns of preceding and following trains to predict potential collision risks in advance, and immediately executes emergency controls if an accident risk is detected.

[0240] Furthermore, an optimal train operation plan is derived through simulation of various operation scenarios and provided to the autonomous driving device (110).

[0241] In this way, in the second stage, safer and more efficient autonomous train operation is realized through the trinity cooperation of the autonomous driving device (110), the railway system (120), and the monitoring and control system (130). Real-time information exchange and organic linkage between each system enable quick and flexible response even in unexpected emergencies.

[0242] The third stage is the termination of train operation and post-operation management stage (S603). When the train arrives at the final stop and completes the operation, the autonomous driving device (110) stores the entire driving record in a database and generates a comprehensive report on the results of the operation. This report records in detail the driving indicators, such as driving distance, time taken, energy consumption, and average speed, as well as any unusual occurrences and warning messages that occurred during the operation.

[0243] The railway system (120) and the monitoring and control system (130) also collect and store all data related to the operation of the train. This includes track condition monitoring data, train control command history, warning and intervention records, etc. These data are subsequently used for analysis of operation results, accident investigation, and long-term system improvement.

[0244] Meanwhile, the monitoring and control system (130) performs post-simulation based on the collected driving data. By reproducing various driving scenarios based on actual driving records, it evaluates the control performance of the autonomous driving device (110) and derives potential areas for improvement. In addition, the simulation results are used as learning data for the continuous evolution and advancement of the autonomous driving algorithm.

[0245] At this stage, regular inspection and maintenance of the system are also carried out. Various sensors and control devices of the autonomous driving device (110), track facilities and operation management modules of the railway system (120), and servers and network equipment of the monitoring and control system (130) are subject to inspection. Any defects or aging parts found are immediately replaced or repaired to prepare for the next operation cycle.

[0246] Operational data and simulation analysis results secured through Phase 3, along with system status information confirmed through regular inspections, are fed back into the initialization process of Phase 1. Through this, the system continues to undergo continuous self-learning and evolution, thereby constantly enhancing the safety and efficiency of autonomous train operation.

[0247] In short, the communication-based train control system (100) using railway signals according to the present invention implements intelligent train operation through a three-stage operation process of initialization, autonomous train operation, and post-management. In each stage, the autonomous driving device (110), the railway system (120), and the monitoring and control system (130) are expected to open a new paradigm for future railway transportation by establishing an organic cooperative system based on high levels of automation and intelligence.

[0248] In particular, the bidirectional active connection device (134) and simulation device (135), which are the core of the present invention, provide a groundbreaking solution that can ensure safety and efficiency even in situations where the operation of multiple trains is required. Furthermore, continuous system advancement utilizing accumulated operation data and simulation analysis results will further enhance the completeness of railway autonomous driving technology.

[0249] As illustrated in FIG. 7, the communication-based train control method using railway signals according to the present invention is largely composed of five detailed steps. Each step includes procedures for information collection, situation awareness, decision-making, control, and verification for railway autonomous driving.

[0250] Step 1 (S701) is the process in which the autonomous driving device (110) receives train current status information from the railway system (120). The railway system (120) transmits real-time data collected through the trackside control equipment (121) and the operation management system (122) to the autonomous driving device (110).

[0251] Here, train current status information encompasses various data regarding the train's operating conditions and track environment. Specifically, it includes driving data such as the train's current location, speed, and acceleration, along with infrastructure data such as track gradient, curvature, and switch status.

[0252] In addition, data regarding operation plans and environmental variables, such as operation schedules, train formation information, and weather conditions, are also transmitted. The autonomous driving device (110) comprehensively analyzes this information to accurately recognize the current operating status of the train and establishes an optimal operation strategy based on this.

[0253] The second stage (S702) is a process of predicting and modeling the train's operational response to various operating conditions and potential failure situations based on the train's current status information received by the autonomous driving device (110) and driving data collected through its own sensors.

[0254] The data processing unit (114) of the autonomous driving device (110) fuses the information received from the railway system (120) and the data collected from the on-board sensor unit (111) in real time. In addition to basic operation data such as the speed, acceleration, and position of the train, this may include information on the interior environment of the passenger compartment, energy consumption, and various system status information.

[0255] Next, the data processing unit (114) utilizes big data analysis techniques and machine learning algorithms to derive correlations and patterns between major variables affecting train operation from the collected vast amount of data. Through this, a dynamic model is generated that can predict changes in train behavior due to changes in the external environment or changes in the internal system state.

[0256] The control unit (113) designs and simulates various operation scenarios based on the derived prediction model. For example, it precisely analyzes the impact of situations such as track changes, adverse weather conditions, and the appearance of obstacles on train operation and seeks an optimal response strategy.

[0257] In addition, the risk levels of various anticipated risk factors during operation, such as the possibility of collision with preceding trains, threats to passenger safety, and failures of major equipment, are evaluated and graded in advance. This enables the establishment of a preemptive response system by predicting the likelihood of risk occurrence and its ripple effects.

[0258] Through such comprehensive situational awareness and predictive modeling, the autonomous driving device (110) is able to make three-dimensional decisions that take into account potential risks and opportunities that are difficult to identify solely from real-time train operation data. This is a key process that is directly linked to enhancing the safety and efficiency of autonomous train operation.

[0259] The third stage (S703) is a process in which the autonomous driving device (110) generates real-time driving data optimized for the safe operation of the train based on the results of predictive modeling. The driving data is implemented as specific train control commands and serves as a guideline for controlling core subsystems such as propulsion, braking, and steering.

[0260] Driving data includes the train's target speed, acceleration and deceleration patterns, braking points, and braking intensity. It also includes information related to the route, such as the direction of travel, track switching locations, and stopping points. Furthermore, various control variables for internal train systems, such as passenger announcements, cabin environment control, and fault management, are reflected in the driving data.

[0261] These driving data are determined by synthesizing the train operation prediction model derived in the previous step and the risk assessment results. Various optimization techniques and decision logic are applied here; for example, an algorithm is used to balance factors such as energy efficiency, ride comfort, and on-time operation while prioritizing the safety of train operation.

[0262] In addition, various constraints must be reflected when generating driving data. Typical examples include physical limitations of train performance, structural constraints of track infrastructure, and restrictions under relevant laws and operating regulations. The autonomous driving device (110) seeks to maximize safety and efficiency by deriving an optimal solution that satisfies these complex requirements.

[0263] Step 4 (S704) is a process in which the monitoring and control system (130) performs real-time monitoring and executes control commands for the autonomous train based on the generated driving data. The monitoring and control system (130) leads smooth information exchange and cooperation between the autonomous driving device (110) and the railway system (120), while simultaneously managing the entire process of the train's autonomous driving.

[0264] The monitoring unit (131) of the monitoring and control system (130) receives real-time driving data and train status information from the autonomous driving device (110) and comprehensively analyzes them. The analysis results are provided to the operator in a visualized form, allowing the operator to intuitively understand the operating status of multiple trains.

[0265] If the driving status of an individual train deviates from the planned operating pattern or if a potential risk is detected in the interaction between trains, the monitoring unit (131) immediately generates a warning message. The warning message includes the identifier of the train, the type and location of the abnormal sign, the level of risk, etc., and helps the operator quickly recognize the situation and respond.

[0266] Meanwhile, the control unit (132) directly intervenes in train control based on monitoring results. Depending on the situation, it issues control commands such as deceleration, acceleration, or stopping to individual trains, or coordinates interactions between multiple trains. In addition, it can order a partial or complete suspension of train operations in the event of an emergency.

[0267] When the control unit (132) executes such high-level intervention, the bidirectional active connection device (134) and the simulation device (135) play a particularly important role. These are core modules that lead cooperative driving between adjacent trains and verify their safety.

[0268] First, the bidirectional active linkage device (134) mediates information exchange and integrated action decisions between the preceding and succeeding trains. It ensures a safe distance by transmitting the driving information of the preceding train to the succeeding train in real time, while also synchronizing the driving flow between the two trains by feeding back the driving status information of the succeeding train to the preceding train.

[0269] Through this, multiple trains operate integrally as if they were a single organic system, maximizing track capacity while minimizing interference between trains and energy waste. This is an important mechanism directly linked to improving transport efficiency.

[0270] Next, the simulation device (135) pre-verifies the operation plan of a group of trains integrated and managed by the bidirectional active connection device (134) to ensure the safety of the train's autonomous driving. By performing a simulation with various scenarios prior to actual operation, the validity of the planned driving command and risk factors are closely analyzed.

[0271] In particular, the simulation device (135) precisely models the train swarm behavior under various operating conditions using the connection strength between adjacent trains and the level of synchronization of driving information as key variables. By doing so, it is possible to diagnose safety in advance and preemptively respond to potential risks, thereby controlling the uncertainty associated with autonomous driving.

[0272] In this way, the bidirectional active connection device (134) and the simulation device (135) play a vital role in enhancing safety and efficiency during the real-time monitoring and control process of the autonomous train. Furthermore, the vast amount of operational data and simulation results collected through the operation of these devices can be utilized to support decision-making throughout railway operations and to establish mid-to-long-term development plans.

[0273] Step 5 (S705) is a post-management process in which the results of autonomous driving operation are analyzed and evaluated, and system improvements are made based on this. When the train arrives at its destination and the operation ends, the autonomous driving device (110) and the monitoring and control system (130) perform a comprehensive performance analysis based on the accumulated operation data.

[0274] The operation results report includes operational indicators such as the train's total mileage and duration, average speed and energy efficiency, and on-time performance. In addition, safety-related information is recorded in detail, including records of abnormal signs and warnings during operation, as well as emergency intervention situations and measures taken.

[0275] As illustrated in FIG. 8, an example of an operation scenario for an autonomous train applying a communication-based train control system and method using railway signals according to the present invention is as follows. This example is intended to specifically present the feasibility and effects of the invention by assuming application on an actual route.

[0276] First, let us look at the step in which a train operation plan is established. The operation management system (122) generates a train operation plan based on the train operation schedule, route map, train formation information, etc. (S801). The generated operation plan includes the departure and arrival times of individual trains, station information, speed limits on the route, etc.

[0277] The driving plan is transmitted to the autonomous driving device (110) and the monitoring and control system (130) and used as basic information necessary for autonomous driving (S802). The autonomous driving device (110) generates an optimal driving profile based on the driving plan, and the monitoring and control system (130) reviews it to verify the safety and validity of the driving plan.

[0278] Next, let's look at the train operation start and autonomous driving stages. When the train picks up passengers at the departure station and finishes preparing for departure, the autonomous driving device (110) starts autonomous driving according to the operation plan (S803). At this time, the autonomous driving device (110) receives real-time operation data, such as track status and signal information, from the railway system (120) and controls the train based on this.

[0279] In the initial operating section, train A runs independently without a preceding train. The autonomous driving device (110) generates an optimal speed profile by considering track conditions and speed limits, and accelerates the train accordingly. It also analyzes information about the track ahead in real time to detect and respond to potential risk factors.

[0280] Subsequently, a subsequent train B enters the same line (S804). The autonomous driving device (110) of train B also performs autonomous driving based on the operation plan, just like train A. However, due to the presence of the preceding train A, train B requires a different method of train control than independent driving.

[0281] At this point, the monitoring and control system (130), which is the core component of the invention, comes into play. The monitoring and control system (130) calculates the distance between the two trains based on the real-time locations of trains A and B, and derives the minimum distance required for safe operation. This information is transmitted to the autonomous driving device (110) of train B and is used for controlling subsequent trains.

[0282] The autonomous driving device (110) of train B adjusts its speed to maintain a safe distance from the preceding train A (S805). If train A decelerates, train B also decelerates accordingly to prevent a collision. Conversely, if train A accelerates, train B increases its speed within the safe distance range to maximize operational efficiency.

[0283] This series of processes is implemented through the operation of the bidirectional active connection device (134). The driving information of the preceding train A is transmitted to train B in real time to control the safety gap, and at the same time, the driving status information of train B is also fed back to train A so that the driving flow of both trains is synchronized.

[0284] At this time, the simulation device (135) simulates in advance whether the operation plan of trains A and B can be safely executed (S806).

[0285] The purpose is to analyze the interaction between trains under various operating scenarios and identify potential risk factors. If a problem situation, such as a risk of collision, is foreseen, the simulation device (135) may request a modification of the operation plan or order emergency intervention.

[0286] Meanwhile, while trains A and B are running in succession, an emergency situation arises where a vehicle malfunction occurs in train A (S807). Due to the vehicle malfunction, train A is forced to make an emergency stop. This becomes a serious risk factor that directly affects the safety of the subsequent train B.

[0287] However, the communication-based train control system of the present invention is capable of responding quickly and flexibly to such unexpected situations. As soon as the autonomous driving device (110) of train A detects a vehicle failure, relevant information is transmitted to the monitoring and control system (130). The monitoring and control system (130) diagnoses the failure situation, notifies train B of the danger, and instructs train A to make an emergency stop.

[0288] In addition, the monitoring and control system (130) establishes a safety braking scenario for train B by comprehensively considering the distance to the stopping point of the preceding train A and track conditions (S808). It derives an optimal deceleration pattern through a simulation device (135) and transmits the result to train B.

[0289] Train A enters an emergency stop, and Train B also safely decelerates according to the emergency braking control of the autonomous driving device (110) (S809). As a result, a collision between the two trains is prevented in advance, and the safety of the passengers is ensured. As such, rapid and accurate information transmission and integrated decision-making under emergency situations can be considered the decisive strengths of the present invention.

[0290] Afterward, when the malfunction of train A is repaired and normal operation becomes possible, the monitoring and control system (130) re-establishes the operation plan for trains A and B (S810). At this time, the safety and efficiency of the modified plan are also thoroughly reviewed through the simulation device (135). The verified modified plan is simultaneously transmitted to the autonomous driving devices (110) of trains A and B, thereby resuming cooperative autonomous driving of the two trains.

[0291] In this way, trains A and B continue safe and efficient autonomous driving even amidst various changing situations (S811). This is possible because there is a communication-based train control system based on the organic collaboration of the autonomous driving device (110), the railway system (120), and the monitoring and control system (130). The core strength of the present invention is that it can manage the overall operation of the trains in an integrated manner while guaranteeing the autonomy of individual trains.

[0292] Meanwhile, when trains A and B arrive at the terminal station, the autonomous driving operation ends (S812). After the operation ends, as previously mentioned, the operation records are organized and analyzed, and the system itself is diagnosed and optimized. The vast amount of collected operation data is utilized to train the autonomous driving algorithm, thereby driving continuous performance improvement.

[0293] The step-by-step scenario of autonomous train operation presented in this example specifically demonstrates the method of realizing the invention applicable to actual railway sites. It clearly illustrates how the communication-based train control system of the present invention functions to simultaneously ensure safety and efficiency in situations where multiple trains share the same track.

[0294] Above all, the roles of the bidirectional active connection device (134) and the simulation device (135) stand out. The former serves as the driving force that enables multiple trains to cooperate and drive as if they were a single organism, while the latter serves as a means to diagnose and guarantee the safety of such cooperative driving in three dimensions. Through the close interaction of the two devices, the efficiency and safety of train operation are harmonized.

[0295] Furthermore, the process of the autonomous driving device (110) making real-time decisions using information received from the railway system (120) demonstrates the essence of the communication-based control method of the present invention. By processing big data in real-time, including driving information of individual trains and track condition information, optimal train control under highly complex situations is achieved.

[0296] Furthermore, the rapid and accurate response system in the event of an abnormal situation is also impressive. In the event of an emergency situation such as a vehicle breakdown, the risk of a subsequent train colliding is prevented in advance through the intervention of the monitoring and control system (130), and an operation plan suitable for the changed situation is quickly established, thereby ensuring the resilience of train operations.

[0297] Accordingly, the present invention specifically exemplifies that safe and efficient autonomous driving of multiple trains is possible based on the organic collaboration of the autonomous driving device (110), the railway system (120), and the monitoring and control system (130), as well as the innovative functions of the bidirectional active connection device (134) and the simulation device (135). Explanation of the symbols

[0298] 100: Communication-based train control system using railway signals 110: Autonomous driving device 111: Sensor section 112: Communications Department 113: Control unit 114: Data Processing Unit 120: Railway System 121: Trackside control equipment 122: Operation Management System 123: Communication Network 130: Monitoring and Control System 131: Monitoring Department 132: Control unit 133: Communications Department 134: Bidirectional active connection device 135: Simulation device 140: Network S301: Preceding train driving data reception stage S302: Analysis of preceding train running status S303: Step to derive optimal driving pattern for trailing train S304: Step for transmitting trailing train driving control command S305: Trailing train driving control stage S306: Receiving trailing train driving status information step S307: Inter-train spacing and relative speed analysis step S308: Emergency Protocol-Based Train Control Intervention Phase S309: Optimal driving pattern update and driving control stage S310: Analysis of actual driving status of trailing train S311: Transmission of trailing train driving information to preceding train S312: Prior train driving status adjustment stage S401: Monitoring stage for synchronization of preceding and succeeding train driving information S402: Train collision risk prediction and judgment stage S403: Integrity determination step between virtual connection trains S404: Train control intervention step in case of virtual connection integrity compromise and collision risk S405: Virtual connection release and independent train control steps S501: Receiving train current status information step S502: Operating Conditions and Failure Situation Prediction Modeling Phase S503: Real-time driving data generation step for optimal safe driving S601: System Initialization Step S602: Real-time information exchange and cooperative control stage during train operation S603: Train Operation Termination and Post-Management Phase S701: Receiving train current status information step S702: Operating Conditions and Failure Situation Prediction Modeling Phase S703: Real-time driving data generation stage for optimal safe driving S704: Real-time monitoring and control of autonomous trains S705: Autonomous Driving Post-Analysis and Improvement Stages S801: Train operation planning stage S802: Operation plan transmission stage for autonomous driving device and monitoring and control system S803: Leading train departure and autonomous driving initiation phase S804: Phase of initiation of follow-up train operation S805: Prior and trailing train spacing control stage S806: Preceding and trailing train safety operation simulation stage S807: Emergency Situation Stage S808: Emergency Braking Scenario Establishment Phase S809: Emergency stop of preceding train and safety braking of subsequent train S810: Operation plan re-establishment stage S811: Train Autonomous Cooperative Driving Continuation Phase S812: Train operation termination stage

Claims

Claim 1 An autonomous driving device, a railway system, and a monitoring and control system are connected via a network, and the autonomous driving device predicts and models train operation responses to various operating conditions and failure situations based on train current status information received from the railway system to generate real-time driving data for safe operation; and the monitoring and control system is a bidirectional active coupling device that, based on real-time bidirectional communication between preceding and succeeding trains, transmits driving information of the preceding train to the succeeding train and actively controls the driving of the succeeding train based thereon, and simultaneously feeds back driving information of the succeeding train to the preceding train to synchronize the driving status of the preceding train with that of the succeeding train, thereby virtually connecting the preceding and succeeding trains as a single formation to automatically adjust the train spacing and maximize train operation efficiency. The system includes a simulation device that, while the preceding and succeeding trains are virtually connected by the bidirectional active connection device, monitors the level of synchronization of driving information between the preceding and succeeding trains in real time to determine the integrity between the virtually connected trains, performs independent signal control and Automatic Train Protection (ATP) functions based on database information storing the monitored data, or simulates safety gaps and branching scenarios between the virtually connected trains to detect potential collision risks in advance, thereby preventing train collisions; wherein the bidirectional active connection device generates and transmits driving control commands for the succeeding train based on driving information received from the preceding train, and simultaneously transmits driving information received from the succeeding train to the preceding train to synchronize the driving status of the preceding train with that of the succeeding train, and the railway system transmits actual train operation data to the autonomous driving device to provide the current train status, wherein the railway system includes trackside control equipment, an operation management system, and a communication network, and the trackside control equipment is composed of field equipment that directly controls train operation, including a track condition monitoring device, a signal, and a track switch.The above monitoring and control system comprehensively analyzes data received from the autonomous driving device and the railway system to generate real-time monitoring and control commands for the autonomous driving device, wherein the monitoring and control system includes a monitoring unit, a control unit, and a communication unit, wherein the monitoring unit comprehensively analyzes data received from the autonomous driving device and the railway system to monitor the train operation status in real time, the control unit generates specific train control commands based on the monitoring results and transmits them to the autonomous driving device, and the communication unit is responsible for data transmission and reception between the autonomous driving device and the railway system, and the monitoring and control system may directly intervene in train operation to perform manual control, and promotes the safe operation of the train by including measures such as forcibly stopping the train, changing the emergency route, or switching to manual mode in the event of an emergency, and when manual control by the monitoring and control system is initiated, the driving command generation function of the autonomous driving device is temporarily suspended, and the railway system controls train operation according to the control of the monitoring and control system, and when the emergency situation is resolved and train operation is normalized, the monitoring and control system terminates manual control and resumes the driving command generation function of the autonomous driving device to return to autonomous driving mode, and the simulation device [is] a train when the integrity between virtually connected trains is compromised When the safety gap between trains narrows below a set range or when the virtual connection is released, an emergency braking signal is output to prevent collision between trains, wherein the simulation device monitors the level of synchronization of driving information between trains in real time by utilizing driving data of preceding and following trains, and calculates the synchronization error between trains by collecting driving data including the speed, acceleration, and position of the preceding and following trains and comparing and analyzing it, and the simulation device predicts and determines a potential collision risk index by analyzing the relative distance and speed information between trains along with the level of synchronization of driving information.The above simulation device is a communication-based train control system using railway signals that predicts the possibility of collision between trains at a future point in time by modeling the change over time of the relative distance and relative speed between trains based on real-time driving data of preceding and succeeding trains. The autonomous driving device (110) comprehensively analyzes the track condition, signal information, and data of the preceding train's position received from the railway system (120), and train operation data collected through its own sensors, to identify the real-time operation situation. After the autonomous driving device (110) derives the operation path, speed, and acceleration / deceleration pattern, and generates a train control command accordingly. The autonomous driving device (110) utilizes predictive modeling technology to identify multiple operation conditions and potential risk factors in advance and simulates the train operation response for multiple scenarios based on accumulated operation data and machine learning algorithms to prepare countermeasures. Claim 2 A communication-based train control system using railway signals according to claim 1, wherein the autonomous driving device (110) assumes situations of track change, signal failure, and adverse weather conditions, predicts and determines the route, speed control, and braking pattern for the safe operation of the train in each situation, pre-diagnoses the risk of collision with a preceding train and the possibility of disaster situations such as fire and flooding, and establishes an emergency response system capable of avoiding risk factors or minimizing damage.

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