Tars-based eoa calculation method, system, device and medium

Abstract

This invention relates to a method, system, device, and medium for EOA calculation based on TARS (Train Autonomous Sensing System), where TARS is the Train Autonomous Sensing System and EOA is the authorized end point of train operation. The method is implemented based on the TARS and the onboard equipment OBS (Onboard System). The TARS autonomously collects track environment data in the train's direction of travel and calculates and outputs the farthest detectable distance ahead. The OBS receives the data output by the TARS and, combined with the operating scenario and equipment status, adaptively performs EOA calculation and outputs the result. Compared with existing technologies, this invention has advantages such as significantly reducing system complexity, filling safety redundancy gaps, improving operational reliability, and significantly reducing integration and modification costs.

Description

Technical Field

[0001] This invention relates to the field of rail transit, and in particular to a method, device and medium for calculating EOA (End of Authority) based on TARS (Train Autonomous Recognition System). Background Technology

[0002] The upgrade of rail transit towards GoA4 level fully automated operation and intelligence is accelerating. The End of Authorization (EOA), as a core parameter balancing safety and efficiency in the train control system, directly determines train intervals, stopping accuracy, and transport capacity. Current mainstream calculations rely on trackside equipment and train-to-ground communication collaboration. However, with metro intervals in core cities approaching 2 minutes (requiring meter-level EOA error), coupled with the need for low-cost, short-cycle upgrades to existing lines, the accuracy, scenario adaptability, and economic viability of EOA have become key bottlenecks restricting the industry's intelligent upgrade. Existing technologies generally suffer from the following problems: 1) Insufficient computational accuracy makes it difficult to balance security and efficiency. Current EOA calculations primarily rely on the traditional architecture of "trackside equipment sensing + vehicle-to-ground communication transmission + static model extrapolation," which suffers from significant accuracy bottlenecks: First, vehicle-to-ground communication has inherent latency (the existing CBTC system can have a latency of up to 0.8 seconds), causing a lag in the synchronization of key data such as track section occupancy status and preceding train position. This, coupled with train positioning drift errors, ultimately results in EOA calculation deviations generally exceeding 5 meters, failing to meet the demands of high-density operations. Second, the calculation model relies excessively on static track parameters (such as fixed braking distances and preset safety margins) and fails to fully integrate real-time train operating conditions (such as load and braking performance degradation). This leads to conservative safety redundancy settings, and on some lines, excessive safety distances reserved at EOA boundaries make it difficult to compress the minimum headway to within 2 minutes, limiting capacity improvement. Third, in scenarios with multiple trains running together, the EOA screening algorithm easily overlooks hidden train risks during the transition between fixed block and moving block sections, requiring the setting of zero-speed limits to ensure safety, further reducing operational efficiency.

[0003] 2) Poor adaptability to complex scenarios and insufficient reliability under extreme conditions. The existing perception system upon which EOA calculations rely has significant shortcomings in complex environments: On the one hand, traditional sensors (ordinary LiDAR, cameras) have limited detection capabilities for low-reflectivity targets (such as black obstacles, wet tracks). Mainstream 905nm LiDARs have a detection range of only 70-100 meters under 10% reflectivity conditions, which further decreases in rain and fog. This results in EOA failing to dynamically adjust due to a lack of effective environmental data in blind spots such as curves and slopes, or in adverse weather conditions, and may even lead to calculation failures. On the other hand, existing methods lack adaptation logic for specific scenarios. For example, when there are no protected sections on the platform, the EOA extension lacks accurate calculation basis, easily leading to trains failing to stop precisely or requiring manual intervention. These problems result in insufficient robustness of EOA calculations in complex scenarios, becoming a major obstacle to the implementation of fully automated driverless vehicles.

[0004] 3) Economic challenges in upgrading existing power lines Upgrading existing rail transit signal systems currently faces significant economic challenges: traditional upgrade methods require covering the entire chain from "line to station to train to control center," replacing core equipment such as area controllers and trackside sensors, resulting in high upgrade costs and long upgrade cycles. Phased construction will still affect peak passenger flow operations.

[0005] To address the aforementioned issues, the rail transit sector has developed targeted technical solutions, which primarily revolve around "hardware upgrades + algorithm optimization + system collaboration." Specific search results are as follows: Chinese patent publication number CN114655276A proposes a full-chain system solution based on "onboard control system - rail star chain system - cloud central control system" at the system level. Its core value lies in reconstructing the rail transit operation architecture and solving the global problems of "schedule-coordination-transformation".

[0006] Chinese patent publication number CN111923966A constructs a binary architecture of "operation control unit + scheduling and command unit". The core innovation is to divide the system into four intelligence levels (L1-L4) (from low to high) and improve the automation and autonomy capabilities at each level.

[0007] Chinese patent publication CN119389271A uses SOTIF risk assessment to iteratively optimize unsafe control behaviors related to EOA (such as a following vehicle entering the path of a preceding vehicle), ensuring the safety of the EOA boundary.

[0008] Chinese patent publication CN117565925A reduces the EOA calculation deviation in the turnout area from ±8 meters to ±3 meters through dual verification of "vehicle positioning + ground perception". However, it only optimizes for a single type of scenario and does not form a fully covered EOA calculation architecture.

[0009] Chinese patent publication CN117565938A proposes a system-level solution for train beyond-line-of-sight perception through hardware-driven upgrades. It provides beyond-line-of-sight / blind zone environmental data for EOA calculation, reducing deviations caused by line-of-sight limitations. However, the power consumption of the UAV itself increases the operating cost of the line, and the economic efficiency for the transformation of long-distance / old lines is still insufficient. The equipment compatibility and subsequent integration costs are also high.

[0010] Therefore, the insufficient accuracy of EOA calculation, poor adaptability to complex scenarios, and insufficient coordination between energy monitoring and train operation permits in existing rail transit train control systems have become technical problems that need to be solved. Summary of the Invention

[0011] The purpose of this invention is to overcome the shortcomings of the existing technology by providing a TARS-based EOA calculation method, system, device, and medium. It aims to detect obstacles ahead of the train by leveraging the autonomous sensing capabilities of TARS, and combines a dual-mode logic of "independent calculation + fusion of trackside data", a backup mode safety redundancy design, and a lightweight adaptation scheme with few interfaces to solve the pain points of insufficient accuracy, weak adaptability to complex scenarios, and high transformation costs of traditional methods. It balances economy and reliability and provides stable support for the high-density, high-precision urban rail operation needs.

[0012] The objective of this invention can be achieved through the following technical solutions: According to a first aspect of the present invention, an EOA calculation method based on TARS is provided, wherein TARS is a train autonomous sensing system and EOA is the authorized end point of train operation. The method is implemented based on the train autonomous sensing system TARS and the on-board equipment OBS. The TARS autonomously collects track environment data in the direction of train operation and calculates and outputs the farthest detectable distance ahead. The on-board equipment OBS receives the data output by TARS and, in combination with the operating scenario and equipment status, adaptively performs EOA calculation and outputs it.

[0013] As a preferred technical solution, the method specifically includes the following steps: Step S1: System initialization and parameter configuration; In step S2, the on-board equipment OBS completes information exchange with the trackside equipment and TARS respectively, and performs data reception and verification. Step S3: Based on the data sent by the trackside equipment and TARS, and combined with the operating scenario and equipment status, OBS adaptively selects the calculation logic. Step S4: Output the calculation results from step S3 and update them dynamically.

[0014] As a preferred technical solution, step S1 specifically includes: Step S101: After the train is powered on, the TARS and OBS synchronously start hardware self-test; Step S102: After the self-test is passed, the preset data is automatically read, including line parameters and train parameters.

[0015] As a preferred technical solution, step S2 specifically includes: Step S201: The OBS synchronously sends real-time train operation status information and train location information to the trackside equipment and TARS. In step S202, the trackside equipment calculates and feeds back the trackside EOA information ZC_EOAlocation to the OBS based on the operating status and location information of all trains in the area. In step S203, TARS uses the train location information sent by OBS to detect the track environment in the direction of train operation, outputs the farthest reachable location ahead TARS_EPOAlocation, and uploads it to OBS in real time. In step S204, OBS performs delay and integrity checks on the trackside equipment data and timestamps the TARS data.

[0016] As a preferred technical solution, in step S202, if the trackside equipment malfunctions, including communication interruption or data verification failure, the trackside data is marked as invalid.

[0017] As a preferred technical solution, step S3 specifically includes: Step S301, under normal operating mode, the OBS only continuously uploads train positioning information to the trackside equipment, and the main control on-board equipment CC receives the trackside EOA information ZC_EOAlocation and performs main control vehicle operation. Step S302: When the backup mode is activated, the OBS starts the safety margin protection mechanism. Taking into account the risks such as the backward slip of the preceding vehicle during the communication delay, the TARS_EPOAlocation is moved back to the upstream of the track by a preset safety distance MaxDistance, TARS_EOAlocation is generated and continuously monitored, and step S303 is executed. Step S303: The OBS performs EOA calculations in backup mode according to different scenarios.

[0018] As a preferred technical solution, step S303 specifically includes: Step S3031: Under normal conditions, the OBS merges TARS_EOAlocation and ZC_EOAlocation, and takes the maximum value of the two as the destination position of the train operation permit, denoted as MAlocation=max(TARS_EOAlocation, ZC_EOAlocation); In step S3032, in the event of an abnormality in the trackside equipment, the OBS uses only TARS_EOAlocation as the end point of the train operation permit, and MAlocation=TARS_EOAlocation, to ensure autonomous and safe operation when there is no trackside support.

[0019] As a preferred technical solution, step S4 specifically includes: Step S401, output EOA in different modes; In step S402, the OBS periodically repeats steps S2 and S3, updating TARS_EPOAlocation and ZC_EOAlocation in real time, and synchronously refreshing Malocation.

[0020] As a preferred technical solution, step S401 specifically includes: Step S4011: In normal operation mode, the OBS does not output vehicle control commands. The main control on-board equipment CC receives the trackside ZC_EOAlocation and updates it dynamically to guide ATO automatic driving. In step S4012, in backup mode, the OBS directly outputs the MAlocation determined in step S303 to the train automatic driving system ATO, and simultaneously sends a backup mode activation flag, with the OBS taking the lead in train control operations.

[0021] As a preferred technical solution, step S402 further includes: if the trackside equipment recovers from abnormality to normal in backup mode, automatically switch to the fusion calculation logic in step S3031 to ensure that the EOA matches the train operation status and track environment changes in real time.

[0022] According to a second aspect of the present invention, a system for the TARS-based EOA calculation method is provided. The system includes a main control on-board device CC1, a main control on-board device CC2, and a trackside device. The system also includes an on-board device OBS, an on-board autonomous sensing radar TARS1, and an on-board autonomous sensing radar TARS2. The on-board autonomous sensing radar TARS1 and the on-board autonomous sensing radar TARS2 are deployed at both ends of the train and are connected to the on-board device OBS through interfaces. The on-board device OBS interacts with the trackside device through an existing communication interface.

[0023] According to a third aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method described thereon.

[0024] According to a fourth aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method described thereon.

[0025] Compared with the prior art, the present invention has the following advantages: (1) The present invention adopts a lightweight architecture design to decouple the perception and computing functions, thereby significantly reducing the system complexity: The system adopts a decoupled architecture of "TARS lightweight perception + on-board equipment centralized computing". TARS is only responsible for collecting track environment data, identifying obstacles and outputting the "farthest reachable position ahead", and does not participate in complex EOA derivation and fusion calculation. All core EOA logic, pattern judgment and data fusion are completed by the on-board equipment (OBS), and the functional boundaries of the equipment are clearly defined. This avoids system redundancy caused by deep binding of multiple devices and overlapping functions in existing technologies, simplifies the troubleshooting process, reduces hardware deployment and maintenance costs, and improves system stability. (2) The interface of this invention is extremely simple and adaptable to existing systems, significantly reducing integration and modification costs: Only one new standard interface is added to interact with the on-board equipment (OBS / TARS). The interface between OBS and the trackside equipment can reuse the communication interface between the main control on-board equipment CC and the trackside equipment and the central dispatch system, which is fully compatible with the existing line interface specifications. This solves the problem that existing technologies require the addition of multiple types of interfaces and have high integration complexity, shortens the existing line transformation cycle, reduces interface development and adaptation costs, and avoids the impact of large-scale system reconstruction on operations. (3) This invention adopts a dedicated backup mode + scenario-based fusion strategy to fill the gap in security redundancy and improve operational reliability: A backup mode has been added to trigger when the main control onboard equipment (CC) fails, and the OBS automatically takes over vehicle control. In backup mode, EOA is calculated in different scenarios: when the trackside equipment is normal, based on the constraint that both TARS and ZC are SIL4 level safety devices, max(TARS_EOAlocation, ZC_EOAlocation) is used for fusion calculation to complement and avoid the blind spots of TARS and the environmental perception defects of ZC; when the trackside equipment is abnormal, only TARS_EOAlocation is used for independent calculation, providing double protection for safety. It fills the gap in existing technology where there is no dedicated EOA backup mechanism. In the event of a main system failure, it can support the safe operation of the train to the next station and avoid operational interruption. At the same time, through the complementary risks of the two systems, it not only ensures safety redundancy but also avoids overly conservative train control caused by equipment defects, thus balancing safety and transport capacity.

[0026] (4) This invention has extremely high adaptability and perfectly meets the needs of existing line renovation: TARS is a lightweight onboard retrofit device that is small in size and low in power consumption and can be directly adapted to existing trains; the EOA calculation logic is integrated into the existing onboard terminal, without the need to replace the trackside equipment or reconstruct the core train control system, and is compatible with different train control systems such as CBTC. It solves the pain points of existing technology in terms of large workload, high cost and long cycle for upgrading existing lines. It is especially suitable for upgrading old lines that have been in operation for more than 10 years, and can quickly improve the safety level and intelligence level of the line without affecting daily operation.

[0027] (5) This invention adopts an adaptive mode switching + dynamic update mechanism to balance full-scene adaptation and real-time requirements: The onboard equipment can adaptively switch between normal operation mode and backup mode, and between independent computing mode and fusion computing mode according to the status of the main control equipment and the validity of the trackside data, with seamless switching process; it can dynamically update TARS_EOAlocation and ZC_EOAlocation according to a preset cycle and refresh the EOA results in real time. It can be adapted to all operational scenarios, including straight sections / curves, normal / abnormal trackside conditions, and normal / downtime main system conditions. It not only ensures the meter-level accuracy requirements of high-density operations, but also solves the existing defects of TARS track corners where the line of sight is obstructed and ZC cannot perceive environmental obstacles, ensuring that EOA matches the train's operating status and track environment in real time. Attached Figure Description

[0028] Figure 1 This is a schematic diagram of the system structure of the present invention; Figure 2 This is a schematic diagram illustrating a specific embodiment of the present invention; Figure 3 This is a flowchart illustrating the specific process of the method of the present invention. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0030] Example 1 This invention addresses the technical pain points of insufficient EOA calculation accuracy, poor adaptability to complex scenarios, and inadequate coordination between energy monitoring and train operation permitting in rail transit train control systems. It proposes a dual-mode integrated solution based on Train Response System (TARS)—serving as a backup mode for traditional EOA calculations and also as a novel autonomous sensing technology for independent train control. Through TARS's built-in low-reflectivity object detection, harsh weather adaptability (IP67 protection, wide-temperature operation), and dynamic target tracking algorithm, it solves the problems of traditional EOA calculations relying on a single data source, obstacle recognition blind spots, and rigid energy control. It also fills the industry gaps of low efficiency in traditional backup modes and the lack of independent train control technology, achieving a triple improvement in EOA calculation accuracy, operational reliability, and train energy consumption optimization. This solution is compatible with mainstream train control systems such as CBTC and can be directly installed on existing trains without large-scale modifications to trackside equipment. It meets the redundancy and backup requirements of existing systems while supporting the implementation of new autonomous train control scenarios, demonstrating strong engineering practicality and technical scalability.

[0031] like Figure 3 As shown, the method of the present invention specifically includes the following steps: Step S1: System Initialization and Parameter Configuration After the train is powered on, the Onboard Autonomous Sensing System (TARS) and the Onboard System (OBS) simultaneously initiate hardware self-tests to verify the effectiveness of core components such as lidar, communication interfaces, and computing modules. After passing the self-test, the system automatically reads preset data (including line parameters such as track gradient, curvature, and speed limit, as well as train parameters such as train braking performance and load reference values). After initialization, the system proceeds to step S2.

[0032] Step S2: Multi-source information interaction and data acquisition OBS, trackside equipment, and TARS exchange information through existing interfaces, specifically including: Step S 21 The OBS synchronously sends real-time train operation status information (current active terminal, train speed, etc.) and train location information to the trackside equipment and TARS. Step S 22 The trackside equipment (such as the area controller ZC) calculates and feeds back the trackside EOA information (denoted as ZC_EOAlocation) to the OBS based on the running status and location information of all trains in the area; if the trackside equipment malfunctions (communication interruption, data verification failure), it marks "trackside data invalid"; Step S 23 TARS uses the train location information sent by OBS to detect the track environment (including obstacle recognition and track clearance detection) in the direction of train operation, outputs the "farthest reachable location ahead", denoted as TARS_EPOAlocation, and uploads it to OBS in real time; Step S 24 1. OBS completes data reception and verification: performs delay and integrity verification on trackside equipment data, timestamp alignment on TARS data, and proceeds to step S3 after verification is completed.

[0033] Step S3: Adaptive Calculation Mode Judgment and EOA Fusion Processing OBS adapts to the operational scenario and device status, and adaptively selects the calculation logic, specifically including: Step S 31 Normal operating mode (main control onboard equipment CC is normal): OBS does not participate in direct vehicle control, but only continuously uploads train positioning information to the trackside equipment. The main control onboard equipment CC receives the trackside ZC_EOAlocation and takes the lead in vehicle control operation, then proceeds to step S4; Step S 32 Backup mode activation (triggered by main control onboard equipment CC failure): OBS activates the safety margin protection mechanism. Considering the risks such as the preceding vehicle rolling backward during communication delays, it backs TARS_EPOAlocation upstream of the track by a preset safety distance, denoted as MaxDistance, generates TARS_EOAlocation and continuously monitors it, then proceeds to step S. 33 ; Step S 33 EOA calculation for different scenarios in backup mode: Scenario A (trackside equipment is normal): OBS merges TARS_EOAlocation and ZC_EOAlocation, and takes the maximum value of the two as the destination position of the driving permission, denoted as MAlocation = max (TARS_EOAlocation, ZC_EOAlocation); Scenario B (trackside equipment malfunction): OBS cuts off trackside data dependency and uses only TARS_EOAlocation as the end point of train operation permission, MAlocation = TARS_EOAlocation, to ensure autonomous and safe operation when there is no trackside support. The core constraint of taking the maximum value in this invention is that both TARS and the trackside ZC are SIL4 level safety devices, and their outputs are legal safe train operation boundaries. Taking the maximum value can achieve risk complementarity between the two independent safety systems, avoiding the shortcomings of TARS in terms of obstructed vision at track corners and slopes, and avoiding the shortcomings of ZC in terms of not being able to perceive obstacles in the track environment, while satisfying the fault-oriented safety principle of the rail transit train control system.

[0034] After completing the above scenario calculations, proceed to step S4.

[0035] Step S4, EOA Output and Dynamic Update Step S 41 EOA output in different modes: Normal operating mode: OBS does not output vehicle control commands. The main control on-board equipment CC receives the trackside ZC_EOAlocation and updates it dynamically to guide ATO automatic driving. Backup mode: OBS will perform step S 33 The confirmed MAlocation is directly output to the Automatic Train Operation (ATO) system, and a "backup mode activated" flag is sent simultaneously, allowing the OBS to take the lead in train control. Step S 42 Dynamic update mechanism: OBS periodically repeats steps S2-S3 to update TARS_EPOAlocation and ZC_EOAlocation (when the trackside is normal) in real time, and refreshes MAlocation synchronously; if the trackside equipment recovers from abnormality to normal in backup mode, it automatically switches to the fusion calculation logic of scenario A to ensure that EOA matches the train operation status and track environment changes in real time.

[0036] The above is an introduction to the method embodiments. The following will further illustrate the solution of the present invention through system and specific case embodiments.

[0037] like Figure 1 As shown, the vehicle architecture of this invention is deployed based on the existing train control system: the existing vehicle equipment units CC1 / CC2 (redundant main control), beacon antenna and coded odometer and other basic equipment are retained, and their original positioning and data interaction functions are maintained; on top of this, the vehicle equipment OBS (core computing module) and TARS1 and TARS2 (vehicle autonomous perception radar) are added, where TARS1 and TARS2 are deployed at both ends of the train and connected to OBS through interfaces. OBS then realizes data interaction with trackside equipment through existing communication interfaces.

[0038] like Figure 2 As shown, in a train tracking scenario, the following train TU1 scans the track using its own TARS2 to sense and output TARS_EPOAlocation (the absolute position of the rear of the preceding train TU2). The following train's OBS module combines the braking model curve and the safety margin MaxDistance to derive TARS_EOAlocation (the safe stopping boundary of the following train). At the same time, the following train's OBS receives ZC_EOAlocation (the safety boundary on the trackside side) calculated by the trackside device ZC through the existing interface. Finally, the following train's OBS merges these two boundaries and outputs a safe driving permit, completing the stable tracking of the preceding train.

[0039] The present invention is based on the above Figure 1 Vehicle architecture and Figure 2 The application scenario design highlights the core innovations and beneficial effects: 1) Decoupling of "perception-computing" functions, resulting in a lightweight architecture: Figure 1 In this design, the TARS acts solely as a pure sensing unit, outputting only the "farthest reachable position ahead" to the OBS. All EOA calculation logic is handled independently by the OBS, with the functional boundaries between the two completely separated. Unlike the existing architecture where "sensing and computing are integrated into the same device," in this design, the TARS and OBS each perform their respective functions. A TARS failure only affects the sensing data input, and the OBS can automatically switch to independent mode. This not only reduces the scope of the failure's impact but also simplifies the device's development and maintenance process, significantly reducing system complexity.

[0040] 2) The interface is extremely simple and adaptable to existing systems: Figure 1 The OBS in China reuses existing communication interfaces to interact with trackside and onboard equipment. Figure 2 The OBS receives trackside ZC data through the existing interface. The entire process only requires the addition of one set of standard interfaces, "TARS-OBS", without the need to reconstruct the existing system links. Functional upgrades are achieved while maintaining compatibility with existing logic, significantly reducing integration and transformation costs.

[0041] Specific examples: The method of the present invention includes the following steps: Step S1: System Initialization and Parameter Configuration After the train is powered on and started, the newly added TARS1 and TARS2 (onboard autonomous sensing radars) and the core computing module OBS complete a hardware self-test. The self-test takes ≤2 seconds. After no fault alarms are detected, preset parameters are read. ① Track parameters (track gradient 0‰, curve radius ∞, straight track); ② Train braking model curve configuration parameters; ③ Safety margin MaxDistance = 10m (including communication delay and braking deviation allowance); ④TARS has a maximum detection range of 0-300m and a detection accuracy of ±0.5m.

[0042] Simultaneously, the train's existing equipment CC1 / CC2, beacon antenna, and coded odometer are initialized synchronously. The beacon antenna receives signals from the track transponder, and the coded odometer collects the number of gear teeth. The two work together to calculate the train's positioning and maintain the original positioning function, providing basic support for subsequent data interaction and calculation in this invention.

[0043] Step S2: Data Interaction and Verification The OBS sends the train's current location information (initial position of the rear train TU1: K1+200.0m) and the train activation terminal End2 to the TARS at both ends of the train through the newly added standard communication interface. After receiving the location information and activation terminal information, the TARS at the driver's cab 2 end starts radar scanning to detect the track environment in the direction of train operation (between K1+200.0m and K1+500.0m), identifies the rear position of the leading train TU2 as K1+400.0m, and then outputs TARS_EPOAlocation=K1+400.0m (absolute position of the rear of the leading train), and transmits the sensing data back to the OBS.

[0044] Meanwhile, the OBS reuses the existing trackside communication interface and receives ZC_EOAlocation=K1+350.0m (the trackside safe stopping boundary, which is derived from the ZC combined with the position of the preceding train TU2 and the braking distance of the following train TU1) based on the train operation status of the entire area through the on-board network. The OBS performs validity verification on the received TARS sensing data and ZC trackside data (data frames are complete and there are no verification errors). After confirming that the data is correct, it proceeds to the next step of mode judgment.

[0045] Step S3: Adaptive Pattern Judgment OBS monitors the operating status of the existing onboard main control equipment (CC) in real time. By reading the status feedback collected via hardwired data (0x00 for normal, 0x01 for fault), it completes the adaptive switching of the operating mode. This judgment is a core branch node, corresponding to two core operating modes: If the CC feedback status frame is 0x00 (normal working state), the train enters normal operation mode and executes step S. 31 The train control logic is led by the CC, and the OBS only acts as a data transmission unit, forwarding the train positioning information (updated in real time) to the trackside ZC, without participating in the calculation and output of EOA. The train follows the existing train control system logic, using the ZC_EOAlocation=K1+350.0m received by the CC as the safety boundary to complete the tracking of the preceding train. The new module in this invention does not interfere with the stable operation of the existing system.

[0046] If the CC suddenly fails, the feedback status frame will be 0x01 (failure status). The train will automatically activate the backup control mode (activation delay ≤100ms, no operational interruption), and execute step S. 32The OBS immediately connects to the core logic of the vehicle control system, activates the preset backup mechanism, and takes the TARS_EPOAlocation=K1+400.0m returned by TARS and combines it with the safety margin MaxDistance=10m. It then corrects the data according to the formula "TARS_EOAlocation=TARS_EPOAlocation-safety margin" to calculate TARS_EOAlocation=K1+390m (the safe parking boundary of the following vehicle). This provides safety data support from the vehicle perception side for subsequent EOA fusion calculations.

[0047] S 33 Scenario-based EOA fusion computing This step is the core calculation in backup mode. OBS, based on the working status of the trackside ZC (judged by data frame verification results; if there is no data for 3 consecutive frames, it is considered abnormal), completes the EOA fusion calculation for both scenarios. This is also the implementation step of the core innovation of this invention. The specific data calculation is as follows: Scenario A: Trackside device ZC is normal, and ZC_EOAlocation data is valid: OBS continuously receives ZC_EOAlocation=K1+350.0m output from trackside ZC (ZC is a safety boundary derived from full-area positioning data after correction). At this time, valid TARS_EOAlocation=K1+390.0m and ZC_EOAlocation=K1+350.0m are obtained. Based on the constraint that both devices are SIL4 level safety devices, the maximum value of the two is taken as the final train operation permission boundary, i.e., MAlocation=max(K1+390.0m, K1+350.0m)=K1+390.0m. In this design, ZC has no blind spots, which can make up for the perception deficiencies of TARS at track corners and slopes; TARS can perceive the track environment in real time (such as sudden deceleration of the train in front), making up for the ZC's inability to identify obstacles, realizing the complementary risks of the two systems, and MAlocation is more in line with actual safety requirements than single perception / trackside data.

[0048] Scenario B: Trackside equipment ZC malfunction, no valid ZC_EOAlocation data: If the trackside ZC is interrupted and the OBS does not receive valid data for 3 consecutive frames (within 300ms), it is determined to be a ZC malfunction. At this time, the OBS directly uses MAlocation=TARS_EOAlocation=K1+390.0m as the train operation permission boundary, and independently controls the train by relying solely on the sensing data of the onboard TARS (updating the position of the preceding train every 100ms). It controls the running speed of the following train TU1 to ≤45km / h, ensuring that the train can still safely track the preceding train until it reaches the next station even without trackside data support.

[0049] Step S4: EOA Output and Dynamic Update This step is the final execution stage of the process and includes step S. 41 Sub-mode output and steps S 42 The system continuously updates two core actions, ensuring real-time data transmission and vehicle control safety throughout the entire process. Step S 41 According to the current train operation mode, the OBS outputs the calculated MAlocation to the train's ATO automatic driving system through the existing interface. Based on this safety boundary, and combined with the distance between the current position of the following train (K1+205.0m, train position update under communication delay) and MAlocation (K1+390.0m), the ATO automatically triggers deceleration or constant speed commands to maintain a safe distance, enabling the following train TU1 to safely track the preceding train TU2 without the risk of rear-end collision.

[0050] Step S 42 During train operation, the OBS repeatedly executes all the logic from steps S2 to S3 at a preset cycle (100ms), updating the data in real time. ①TARS scans the position of the vehicle in front every 100ms. If the vehicle in front decelerates TU2, TARS_EPOAlocation is updated to K1+340.0m. OBS synchronously recalculates TARS_EOAlocation=K1+340.0m-10m=K1+330.0m. ② If the trackside ZC returns to normal, the OBS receives the new ZC_EOAlocation=K1+400.0m and re-merges it to obtain MAlocation=K1+400.0m. This dynamic update mechanism can adapt to real-time operating conditions such as the movement of the preceding train and changes in the track environment, ensuring that the train operation permission boundary always matches the actual operating status of the train and guaranteeing the safe operation of the train throughout the entire journey.

[0051] Example 2 This invention also provides an electronic device including a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) or loaded from a storage unit into a random access memory (RAM). The RAM may also store various programs and data required for device operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0052] Multiple components in the device are connected to the I / O interface, including: input units such as keyboards and mice; output units such as various types of displays and speakers; storage units such as disks and optical discs; and communication units such as network interface cards (NICs), modems, and wireless transceivers. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0053] The processing unit executes the various methods and processes described above, such as methods S1 to S4. For example, in some embodiments, methods S1 to S4 may be implemented as computer software programs tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of methods S1 to S4 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute methods S1 to S4 by any other suitable means (e.g., by means of firmware).

[0054] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0055] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0056] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0057] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for TARS-based EOA computation, characterized in that, Wherein TARS stands for Train Autonomous Sensing System and EOA stands for Train Authorization End Point. The method is based on the Train Autonomous Sensing System TARS and the Onboard Equipment OBS. The TARS autonomously collects track environment data in the direction of train operation and calculates and outputs the farthest detectable distance ahead. The Onboard Equipment OBS receives the data output by the TARS and, in combination with the operating scenario and equipment status, adaptively calculates and outputs the EOA.

2. The TARS-based EOA calculation method according to claim 1, characterized in that, The method specifically includes the following steps: Step S1: System initialization and parameter configuration; In step S2, the on-board equipment OBS completes information exchange with the trackside equipment and TARS respectively, and performs data reception and verification. Step S3: Based on the data sent by the trackside equipment and TARS, and combined with the operating scenario and equipment status, OBS adaptively selects the calculation logic. Step S4: Output the calculation results from step S3 and update them dynamically.

3. The TARS-based EOA calculation method according to claim 2, characterized in that, Step S1 specifically involves: Step S101: After the train is powered on, the TARS and OBS synchronously start hardware self-test; Step S102: After the self-test is passed, the preset data is automatically read, including line parameters and train parameters.

4. The TARS-based EOA calculation method according to claim 2, characterized in that, Step S2 specifically involves: Step S201: The OBS synchronously sends real-time train operation status information and train location information to the trackside equipment and TARS. In step S202, the trackside equipment calculates and feeds back the trackside EOA information ZC_EOAlocation to the OBS based on the operating status and location information of all trains in the area. In step S203, TARS uses the train location information sent by OBS to detect the track environment in the direction of train operation, outputs the farthest reachable location ahead TARS_EPOAlocation, and uploads it to OBS in real time. In step S204, OBS performs delay and integrity checks on the trackside equipment data and timestamps the TARS data.

5. The method of claim 4, wherein, In step S202, if the trackside equipment malfunctions, including communication interruption or data verification failure, the trackside data is marked as invalid.

6. The method of claim 4, wherein, Step S3 specifically involves: Step S301, under normal operating mode, the OBS only continuously uploads train positioning information to the trackside equipment, and the main control on-board equipment CC receives the trackside EOA information ZC_EOAlocation and performs main control vehicle operation. Step S302: When the backup mode is activated, the OBS starts the safety margin protection mechanism. Taking into account the risks such as the backward slip of the preceding vehicle during the communication delay, the TARS_EPOAlocation is moved back to the upstream of the track by a preset safety distance MaxDistance, TARS_EOAlocation is generated and continuously monitored, and step S303 is executed. Step S303: The OBS performs EOA calculations in backup mode according to different scenarios.

7. The method of claim 6, wherein, Step S303 specifically includes: Step S3031: Under normal conditions, the OBS merges TARS_EOAlocation and ZC_EOAlocation, and takes the maximum value of the two as the destination position of the train operation permit, denoted as MAlocation=max(TARS_EOAlocation, ZC_EOAlocation); In step S3032, in the event of an abnormality in the trackside equipment, the OBS uses only TARS_EOAlocation as the end point of the train operation permit, and MAlocation=TARS_EOAlocation, to ensure autonomous and safe operation when there is no trackside support.

8. The method of claim 7, wherein, Step S4 specifically involves: Step S401, output EOA in different modes; In step S402, the OBS periodically repeats steps S2 and S3, updating TARS_EPOAlocation and ZC_EOAlocation in real time, and synchronously refreshing Malocation.

9. The method of claim 8, wherein, Step S401 specifically involves: Step S4011: In normal operation mode, the OBS does not output vehicle control commands. The main control on-board equipment CC receives the trackside ZC_EOAlocation and updates it dynamically to guide ATO automatic driving. In step S4012, in backup mode, the OBS directly outputs the MAlocation determined in step S303 to the train automatic driving system ATO, and simultaneously sends a backup mode activation flag, with the OBS taking the lead in train control operations.

10. The method of claim 8, wherein, Step S402 further includes: if the trackside equipment recovers from an abnormality to normal in backup mode, automatically switch to the fusion calculation logic in step S3031 to ensure that the EOA matches the train operating status and track environment changes in real time.

11. A system for the TARS-based EOA calculation method of any of claims 1-10, the system comprising a master on-board device CC1, a master on-board device CC2 and a trackside device, characterized in that, The system also includes an onboard device OBS, an onboard autonomous sensing radar TARS1, and an onboard autonomous sensing radar TARS2. The onboard autonomous sensing radar TARS1 and the onboard autonomous sensing radar TARS2 are deployed at both ends of the train and are connected to the onboard device OBS through interfaces. The onboard device OBS interacts with the trackside equipment through existing communication interfaces.

12. An electronic device comprising a memory and a processor, said memory having stored thereon a computer program, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 10.

13. A computer readable storage medium having stored thereon a computer program, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 10.

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