Statistical Model Checking for Networks of Priced Timed Automata

Lecture Notes in Computer Science - Formal Modeling and Analysis of Timed Systems ◽

10.1007/978-3-642-24310-3_7 ◽

2011 ◽

pp. 80-96 ◽

Cited By ~ 71

Author(s):

Alexandre David ◽

Kim G. Larsen ◽

Axel Legay ◽

Marius Mikučionis ◽

Danny Bøgsted Poulsen ◽

...

Keyword(s):

Model Checking ◽

Statistical Model ◽

Timed Automata ◽

Statistical Model Checking

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On Creation and Analysis of Reliability Models by Means of Stochastic Timed Automata and Statistical Model Checking: Principle

Leveraging Applications of Formal Methods, Verification and Validation: Foundational Techniques - Lecture Notes in Computer Science ◽

10.1007/978-3-319-47166-2_11 ◽

2016 ◽

pp. 166-181 ◽

Cited By ~ 2

Author(s):

Josef Strnadel

Keyword(s):

Model Checking ◽

Statistical Model ◽

Timed Automata ◽

Statistical Model Checking ◽

Reliability Models

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Formal Verification of Heuristic Autonomous Intersection Management Using Statistical Model Checking

Sensors ◽

10.3390/s20164506 ◽

2020 ◽

Vol 20 (16) ◽

pp. 4506

Author(s):

Aaditya Prakash Chouhan ◽

Gourinath Banda

Keyword(s):

Model Checking ◽

Statistical Model ◽

Formal Verification ◽

Autonomous Vehicles ◽

Timed Automata ◽

Autonomous Systems ◽

Statistical Model Checking ◽

Stage Of Development ◽

Verification Techniques ◽

Intersection Management

Autonomous vehicles are gaining popularity throughout the world among researchers and consumers. However, their popularity has not yet reached the level where it is widely accepted as a fully developed technology as a large portion of the consumer base feels skeptical about it. Proving the correctness of this technology will help in establishing faith in it. That is easier said than done because of the fact that the formal verification techniques has not attained the level of development and application that it is ought to. In this work, we present Statistical Model Checking (SMC) as a possible solution for verifying the safety of autonomous systems and algorithms. We apply it on Heuristic Autonomous Intersection Management (HAIM) algorithm. The presented verification routine can be adopted for other conflict point based autonomous intersection management algorithms as well. Along with verifying the HAIM, we also demonstrate the modeling and verification applied at each stage of development to verify the inherent behavior of the algorithm. The HAIM scheme is formally modeled using a variant of the language of Timed Automata. The model consists of automata that encode the behavior of vehicles, intersection manager (IM) and collision checkers. To verify the complete nature of the heuristic and ensure correct modeling of the system, we model it in layers and verify each layer separately for their expected behavior. Along with that, we perform implementation verification and error injection testing to ensure faithful modeling of the system. Results show with high confidence the freedom from collisions of the intersection controlled by the HAIM algorithm.

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A Formal Approach for RT-DVS Algorithms Evaluation Based on Statistical Model Checking

Mathematical Problems in Engineering ◽

10.1155/2015/815230 ◽

2015 ◽

Vol 2015 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Shengxin Dai ◽

Mei Hong ◽

Bing Guo ◽

Yang He ◽

Qiongyu Zhang ◽

...

Keyword(s):

Model Checking ◽

Statistical Model ◽

Timed Automata ◽

Formal Approach ◽

Statistical Model Checking ◽

Novel Approach ◽

Utilization Bound ◽

Statistical Queries ◽

Save Energy ◽

Time Systems

Energy saving is a crucial concern in embedded real time systems. Many RT-DVS algorithms have been proposed to save energy while preserving deadline guarantees. This paper presents a novel approach to evaluate RT-DVS algorithms using statistical model checking. A scalable framework is proposed for RT-DVS algorithms evaluation, in which the relevant components are modeled as stochastic timed automata, and the evaluation metrics including utilization bound, energy efficiency, battery awareness, and temperature awareness are expressed as statistical queries. Evaluation of these metrics is performed by verifying the corresponding queries using UPPAAL-SMC and analyzing the statistical information provided by the tool. We demonstrate the applicability of our framework via a case study of five classical RT-DVS algorithms.

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