scholarly journals Specifying Safety Monitors for Autonomous Systems Using Model-Checking

2014 ◽  
pp. 262-277 ◽  
Author(s):  
Mathilde Machin ◽  
Fanny Dufossé ◽  
Jean-Paul Blanquart ◽  
Jérémie Guiochet ◽  
David Powell ◽  
...  
Keyword(s):  
Model Checking ◽  
Autonomous Systems

scholarly journals Formal Verification of Heuristic Autonomous Intersection Management Using Statistical Model Checking

Sensors ◽  
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.

scholarly journals Algorithmic Ethics: Formalization and Verification of Autonomous Vehicle Obligations

2021 ◽  
Vol 5 (4) ◽  
pp. 1-25
Author(s):  
Colin Shea-Blymyer ◽  
Houssam Abbas
Keyword(s):  
Model Checking ◽  
Deontic Logic ◽  
Autonomous Vehicle ◽  
Autonomous Systems ◽  
Automatic Reasoning ◽  
Starting Point ◽  
Formal Framework ◽  
Act Utilitarianism ◽  
Self Driving Cars

In this article, we develop a formal framework for automatic reasoning about the obligations of autonomous cyber-physical systems, including their social and ethical obligations. Obligations, permissions, and prohibitions are distinct from a system's mission, and are a necessary part of specifying advanced, adaptive AI-equipped systems. They need a dedicated deontic logic of obligations to formalize them. Most existing deontic logics lack corresponding algorithms and system models that permit automatic verification. We demonstrate how a particular deontic logic, Dominance Act Utilitarianism (DAU) [23], is a suitable starting point for formalizing the obligations of autonomous systems like self-driving cars. We demonstrate its usefulness by formalizing a subset of Responsibility-Sensitive Safety (RSS) in DAU; RSS is an industrial proposal for how self-driving cars should and should not behave in traffic. We show that certain logical consequences of RSS are undesirable, indicating a need to further refine the proposal. We also demonstrate how obligations can change over time, which is necessary for long-term autonomy. We then demonstrate a model-checking algorithm for DAU formulas on weighted transition systems and illustrate it by model-checking obligations of a self-driving car controller from the literature.

scholarly journals GRAVITAS: A model checking based planning and goal reasoning framework for autonomous systems

2021 ◽  
Vol 97 ◽  
pp. 104091
Author(s):  
Hadrien Bride ◽  
Jin Song Dong ◽  
Ryan Green ◽  
Zhé Hóu ◽  
Brendan Mahony ◽  
...  
Keyword(s):  
Model Checking ◽  
Autonomous Systems ◽  
Goal Reasoning

scholarly journals Probabilistic Model Checking and Autonomy

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Marta Kwiatkowska ◽  
Gethin Norman ◽  
David Parker
Keyword(s):  
Model Checking ◽  
Probabilistic Model ◽  
Stochastic Games ◽  
Cooperative Behavior ◽  
Autonomous Systems ◽  
Probabilistic Model Checking ◽  
Annual Review ◽  
Publication Date ◽  
Modeling And Analysis ◽  
Markov Decision

The design and control of autonomous systems that operate in uncertain or adversarial environments can be facilitated by formal modeling and analysis. Probabilistic model checking is a technique to automatically verify, for a given temporal logic specification, that a system model satisfies the specification, as well as to synthesize an optimal strategy for its control. This method has recently been extended to multiagent systems that exhibit competitive or cooperative behavior modeled via stochastic games and synthesis of equilibria strategies. In this article, we provide an overview of probabilistic model checking, focusing on models supported by the PRISM and PRISM-games model checkers. This overview includes fully observable and partially observable Markov decision processes, as well as turn-based and concurrent stochastic games, together with associated probabilistic temporal logics. We demonstrate the applicability of the framework through illustrative examples from autonomous systems. Finally, we highlight research challenges and suggest directions for future work in this area. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 5 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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