Towards Safety-Risk Prediction of CBTC Systems With Deep Learning and Formal Methods

Liu, Jing; Qian, Li Ping; Zhang, Yan; Han, Jiazhen; Sun, Junfeng

doi:10.1109/access.2020.2967634

Cited by 8 publications

(4 citation statements)

References 20 publications

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“…Liu et al 39 adopt statistical model checking to calculate failure rates to guarantee vehicles safe operations.…”

Section: Statistical Model Checking and Accident Predictionsmentioning

confidence: 99%

“…As a new application and research direction, we can use statistical model checking for accident or failure predictions, Calder and Sevegnani 7 propose a stochastic framework considering discrete space and temporal logic to forecast failure time bounds and maintenance cost. Liu et al 39 adopt statistical model checking to calculate failure rates to guarantee vehicles safe operations.…”

Section: Literature Review and Research Motivationmentioning

confidence: 99%

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An accident prediction architecture based on spatio‐clock stochastic and hybrid model for autonomous driving safety

Wang

Huang

et al. 2021

Concurrency and Computation

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Summary Collaborative and autonomous driving vehicles combine hardware and software complex processes, also are heavily dependent on and influenced by the world of physical and cyber interactions. They have enabled many new features and advanced functionalities, such as stochastic and hybrid natures, mobile spatial topologies, and time‐critical dependability. However, the existing modeling and verification techniques have not established faith in proving correctness and safety. Spatial and time collision avoidance remains crucial obstacles on the path to becoming ubiquitous and dependable. In order to ensure safety, we first design an accident prediction architecture in system design‐time and run‐time stages. We apply it on collaborative and autonomous overtaking systems involving spatial‐ and time‐critical accident predictions. Then, we develop a novel and dedicated spatio‐clock stochastic specification language (SCSSL) to describe safety invariants and guards in domain‐specific autonomous driving systems. Next, we create the spatio‐clock stochastic and hybrid automata models based on SCSSL in order to model inherently stochastic and hybrid behaviors. To illustrate the effectiveness of spatio‐clock consistency stochastic specification and verification, we adopt statistical model checking natively to provide reliable predictions for the incoming collision instants and positions. Finally, we present an illustrative overtaking case study to verify spatio‐clock stochastic and hybrid related properties and ensure correct modeling, and demonstrate the significance of our proposed approach.

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“…Liu et al 39 adopt statistical model checking to calculate failure rates to guarantee vehicles safe operations.…”

Section: Statistical Model Checking and Accident Predictionsmentioning

confidence: 99%

Section: Literature Review and Research Motivationmentioning

confidence: 99%

An accident prediction architecture based on spatio‐clock stochastic and hybrid model for autonomous driving safety

Wang

Huang

et al. 2021

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…Sydney trains conducted condition monitoring for inspections and prevention of overhead wiring teardowns using laser and computer vision technologies [163]. Similarly, deep learning has been implemented to conduct traffic signal detection [164], [165], predict train delays [166], detect rail fastener defects and ballast history [167]- [169], detect cracks in and the shape and location of bolts [170], inspect railway ties [134], predict safety risks in communication-based train control systems (CBTCs) [171] and to perform subgrade status inspections [172].…”

Section: Related Work In Railway Systemsmentioning

confidence: 99%

A Deep Learning Approach Towards Railway Safety Risk Assessment

Alawad

Kaewunruen

2020

IEEE Access

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Railway stations are essential aspects of railway systems, and they play a vital role in public daily life. Various types of AI technology have been utilised in many fields to ensure the safety of people and their assets. In this paper, we propose a novel framework that uses computer vision and pattern recognition to perform risk management in railway systems in which a convolutional neural network (CNN) is applied as a supervised machine learning model to identify risks. However, risk management in railway stations is challenging because stations feature dynamic and complex conditions. Despite extensive efforts by industry associations and researchers to reduce the number of accidents and injuries in this field, such incidents still occur. The proposed model offers a beneficial method for obtaining more accurate motion data, and it detects adverse conditions as soon as possible by capturing fall, slip and trip (FST) events in the stations that represent high-risk outcomes. The framework of the presented method is generalisable to a wide range of locations and to additional types of risks.

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“…Around the year 2017, Farooq et al [7] evaluated the wireless communication network performance of the CBTC system and suggested that the system could be further optimized; Farooq and Soler [8] did a survey on the CBTC system, and the survey showed that many researchers believe that the combination of CBTC system and Internet of Things technology could develop the CBTC system. Specially, in the research of Wang et al [18,19] around 2019, they focused on the safety protection and communications of the CBTC system, and they improved the CBTC system's wireless communication system by the Q-learning method based on LTE-T2T and obtained excellent application results. Recently, Liu et al [20] focused on combining the CBTC system with artificial intelligence computing (AI Computing).…”

Section: Introduction: the Development Of The Mining Industry And The Cbtc Systemmentioning

confidence: 99%

The Function Design for the Communication-Based Train Control (CBTC) System: How to Solve the Problems in the Underground Mine Rail Transportation?

2021

ASI

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With the continuous development of the mining industry, the world’s major mines have gradually entered the intelligent stage. In intelligent underground mines, the operation roads of the underground transportation equipment are very complicated, and the monitoring and control of the underground traffic have become the problems to be solved in the intelligent underground mines. Therefore, on the basis of solving the practical problems of underground mines, the concept paper discusses the possibility of the communication-based train control (CBTC) system being applied to underground mines through the summary and induction of the related literature. As mining engineers, we have proposed the function design for the CBTC system to solve the problems in underground mine rail transportation, but we still need to continue to work hard for the future development of the underground mines. The concept paper serves as a guide to the Tossing out a brick to get a jade gem, and it has implications for the development and the future of underground mine transportation.

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Towards Safety-Risk Prediction of CBTC Systems With Deep Learning and Formal Methods

Cited by 8 publications

References 20 publications

An accident prediction architecture based on spatio‐clock stochastic and hybrid model for autonomous driving safety

An accident prediction architecture based on spatio‐clock stochastic and hybrid model for autonomous driving safety

A Deep Learning Approach Towards Railway Safety Risk Assessment

The Function Design for the Communication-Based Train Control (CBTC) System: How to Solve the Problems in the Underground Mine Rail Transportation?

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