Automatic obstacle detection is of great significance for improving the safety of train operation. However, the existing autonomous operation of trains mainly depends on the signaling control system and lacks the extra equipment to perceive the environment. To further enhance the efficiency and safety of the widely deployed fully automatic operation (FAO) systems of the train, this study proposes an intelligent obstacle detection system based on deep learning. It collects perceptual information from industrial cameras and light detection and ranging (LiDAR), and mainly implements the functionality including rail region detection, obstacle detection, and visual–LiDAR fusion. Specifically, the first two parts adopt deep convolutional neural network (CNN) algorithms for semantic segmentation and object detection to pixel-wisely identify the rail track area ahead and detect the potential obstacles on the rail track, respectively. The visual–LiDAR fusion part integrates the visual data with the LiDAR data to achieve environmental perception for all weather conditions. It can also determine the geometric relationship between the rail track and obstacles to decide whether to trigger a warning alarm. Experimental results show that the system proposed in this study has strong performance and robustness. The system perception rate (precision) is 99.994% and the recall rate reaches 100%. The system, applied to the metro Hong Kong Tsuen Wan line, effectively improves the safety of urban rail train operation.
Accident causal scenario can describe the process logic of the accident clearly and concretely from the perspective of the control mechanism. Only by improving the quality of the causal scenario can the effective control measures be taken. Combining the technical characteristics of the fully automatic operation (FAO) system, the paper proposes an automated accident causal scenario identification method for FAO system based on the System-Theoretic Process Analysis (STPA) method. Aiming at the problem that there are too many layers in the hierarchical control structure diagram of STPA method, which makes it impossible to effectively trace the cause and the problem that the basic control structure model only contains the control structural information and lacks the cause information, a new basic control structure model is defined to model multiple control processes in time sequence, and then the paper extends it from four aspects: control action, input variables, external disturbance, and synchronous timing to add more system cause information. For the lack of a unified standard description problem for the causal scenario, a fourstage causal scenario description method is defined, this paper has developed the first timing, non-first timing, synchronous timing, and external disturbance causal scenario search rules to ensure the automatic identification of the causal scenarios. Applying the automated safety analysis method to the case study of the operational scenarios of parking in a station of Beijing Yanfang Line, the automated identification of related causal scenarios is successfully completed through the Auto-STPA platform, and corresponding safety requirements are added. The feasibility of the method and the applicability to the analysis of operational scenarios are verified. INDEX TERMSCausal scenario, fully automatic operation system, systems-theoretic process analysis (STPA).
The safety and the correctness of signaling system not only relate to the safety and efficiency of the rail transit operation, but also link with the life safety of passengers. In order to guarantee the safety of a signaling system for metro, the safety certificate for the trial operation with carrying passengers must be obtained. In this paper, a suitable safety management and signaling system integration model are explored according to the CENELEC standards and applied in China. With taking account of the strict safety requirements for the Communication-Based Train Control (CBTC) system, a safety assurance and assessment method based on safety verification and validation process was put forward. This method was applied in every phase of the CBTC system development life cycle to monitor and control each activity in the life cycle and to review each document in system development process. At the same time, this method is also used to ensure the traceability of relevant documents and to test all the functions of the whole system sufficiently and completely. So that the safety operation of train control system can be ensured. Up to now, the independently developed CBTC system with the safety management had been applied in many urban rail transit lines of Beijing, such as Yizhuang Line, Changping Line, Line No. 14, and Line No. 7. The CBTC signaling systems of these projects have been authorized by the safety certification from a third party, e.g., Lloyd Register which is a British company and famous for the safety verification and validation process.
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