System Probability Distribution Modeling Using MDDs

Thornton, Mitchell A.; Manikas, Theodore W.; Szygenda, Stephen A.; Nagayama, Shinobu

doi:10.1109/ismvl.2014.42

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…And, by analyzing a system many times while advancing time in a small step, we can perform transient analysis of an MSS [18].…”

Section: B Transient Analysis Of Multi-state Systemsmentioning

confidence: 99%

“…This is because their time complexity is O(m 3n ), where m is the number of states, and n is the number of components in an MDD [3], [4]. For faster transient analysis of large MSSs, an MDD based method has been proposed [18]. This method can make a transient analysis in the same way as a steadystate analysis by assigning dynamic probability distributions to edges in an MDD.…”

Section: Introductionmentioning

confidence: 99%

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Edge Reduction for EVMDDs to Speed Up Analysis of Multi-state Systems

Nagayama

Sasao

Butler

et al. 2015

2015 IEEE International Symposium on Multiple-Valued Logic

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Abstract-This paper proposes a new reduction rule for edge-valued multi-valued decision diagrams (EVMDDs), which improves the speed of analysis of multi-state systems (MSSs). Existing reduction rules for decision diagrams remove redundant nodes, while the proposed rule removes redundant edges in EVMDDs. Since the time to do an analysis in an MSS depends on the number of edges in the EVMDD, the proposed rule is faster especially when used with edge minimization algorithms based on variable grouping. Experimental results show that the proposed rule reduces the number of edges by up to 30%, and this results in an analysis time that is reduced by up to 30%.

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“…And, by analyzing a system many times while advancing time in a small step, we can perform transient analysis of an MSS [18].…”

Section: B Transient Analysis Of Multi-state Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Edge Reduction for EVMDDs to Speed Up Analysis of Multi-state Systems

Nagayama

Sasao

Butler

et al. 2015

2015 IEEE International Symposium on Multiple-Valued Logic

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“…The system behavior with dependence on the subsystems or subfunctions can be summarized in a system truth table (see e.g. (Rahmat et al 2006) (Thornton et al 2014)). The present paper will provide a truth table for the sensing system of an autonomous driving system.…”

Section: Introductionmentioning

confidence: 99%

Overall Markov Diagram Design and Simulation Example for Scalable Safety Analysis of Autonomous Vehicles

Häring,

Mopuru,

Walz

et al. 2023

Proceeding of the 33rd European Safety and Reliability Conference

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Markov models are a promising tool regarding the assessment of availability, safety, security, and reliability of autonomous driving functions. The paper addresses challenges regarding the overall system functional and static modeling and related overall Markov diagram design options. To this end, the model space is presented, extending the main functions consisting of extended sensory system, decision and control, and vehicle platform manipulation. Sample transition models from literature are used. It is shown how to color-label overall Markov system product states in terms of the level of their criticality, independent of the multiplicity of failures. This is used to model the effect of structural and functional redundancies, e.g., of redundant sensors and sensors of different technology. The modeling approach allows to compare the effect of redundancy options on a systemic level, as well as to identify the need for further aggregation or subdivision of Markov states or refinement of the transition modeling and simulation approach. For instance, in terms of providing statistical assessment of historic events or by using simulation results of specific autonomous driving scenarios, e.g., interaction with vulnerable road users in case of darkness, bad weather, and partial sensor degradation. The paper presents Markov modeling results with a focus on modeling of redundancies of sensors.

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