Automotive safety verification under temporal failure of adaptive cruise control system using statistical model checking

Atallah, Ayman A.; Hamad, Ghaith Bany; Mohamed, Otmane Aït

doi:10.1109/edis.2017.8284024

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…44 As shown in formulas ( 12), ( 13), ( 14), there exists a transition path that satisfies each state-based formula respectively. As for formula (12), it states that cars A and B are on the considering road, we can calculate the possible collision time 10s through invoking the time-to-collision function. 10 E <> car[0].on − the − road and car [1].on − the − road and time − to − collision(0, 1) <= 10,…”

Section: Model Verification For the Autonomous Driving Systemmentioning

confidence: 99%

“…Arcile et al 10 introduce the VerifCar framework devoted to verify communicating autonomous vehicles decision policies by means of formal timed automata. In order to investigate the impact of temporal failures, the work 12 provides a new probabilistic priced timed automata model and do verification using the statistical model checking technique. Klös et al 13 propose a meta‐adaptation layer to extend the MAPE‐K (monitor‐analysis‐plan‐execute plus knowledge) architecture, and use timed automata run‐time model to demonstrate effectiveness of the comprehensible and dependable design approach.…”

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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