A formal framework for the safe design of the Autonomous Driving supervision

Cuer, Romain; Praly, Laurent; Niel, Éric; Diallo, Saïdou; Minoiu-Enache, Nicoleta; Dang-Van-Nhan, Christophe

doi:10.1016/j.ress.2018.01.014

Cited by 12 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For every car I ∈ I and n ∈ L, I may claim its adjacent lane clm ′ in formula (7). Also, it may withdraw this command in formula (8).…”

Section: Spatio-clock Constraint Transitionsmentioning

confidence: 99%

“…Cyber-physical interactions with open and stochastic environment consist of several safety-critical specification dimensions, in which spatial and time are two of most important aspects. 4 Collaborative and autonomous driving safety involves spatial collision freedoms, 5,6 ensuring continuous services in the case of system failures, 7,8 and rigorously temporal and physical timing constraints. 9,10 There also exist spatio-time interactive constraint autonomous behaviors, 11 for example, if an autonomous vehicle wants to overtake another one in the same lane, this behavior should satisfy safe distance condition, temporal or physical time constraint, as well as space_constrained_by_time or time_constrained_by_space, named as spatio-time constraint relations.…”

mentioning

confidence: 99%

See 1 more Smart Citation

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

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.

show abstract

“…For every car I ∈ I and n ∈ L, I may claim its adjacent lane clm ′ in formula (7). Also, it may withdraw this command in formula (8).…”

Section: Spatio-clock Constraint Transitionsmentioning

confidence: 99%