The research team led by HASUO Ichiro at the National Institute of Informatics (NII, Japan) developed a methodology to provide strong mathematical safety guarantees to automated driving vehicles, together with its underlying theory on formal logic. This research was conducted under the ERATO MMSD project funded by the Japan Science and Technology Agency (JST, Japan).
Building on the existing methodology called “Responsibility-Sensitive Safety (RSS)” for mathematical proofs of automated driving safety, the research established its extension called “Goal-Aware RSS (GA-RSS)” that expands RSS’s application domain to a variety of real-world driving scenarios. Specifically, the techniques in GA-RSS derived from theoretical results in formal logic enable one to provide mathematical safety proofs to more complex driving scenarios than before, especially those which require achievement of certain goals such as an emergency stop.
The outcome of this research was published in IEEE Transactions on Intelligent Vehicles, a top journal on automated driving on July 5, 2022 (US Eastern Time).
New method for automatic and efficient discovery of reliable gas turbine system designs
— Exploiting the logical specification in black box optimization and applying it to the design process of real commercial products — A research team consisting mainly of SATO Sota and HASUO Ichiro at the National Institute of Informatics (NII, Japan) and the Graduate University for Advanced Studies (SOKENDAI, Japan), developed a method for automatically finding gas turbine control system designs that satisfy multiple requirements, with collaboration of Mitsubishi Heavy Industries, Ltd.
Automatic control system design methods have not hitherto been able to discover controllers of comparable quality to those designed by human experts. But with the new method developed by this research team, it is now possible to find controllers with comparable quality to the results obtained manually by human experts, based on fully automatic computation. This method is generally applicable to “black box” control systems whose internal behavior cannot be described by models such as mathematical formulas. The method is expected to be useful in design processes in various fields such as autonomous driving.
This research was conducted under the ERATO MMSD Project（*1） funded by the Japan Science and Technology Agency (JST, Japan). The results of this research will be presented online on November 24, 2021 (Chinese Standard Time) at the 24th International Formal Methods Symposium (FM’21).
A team consisting of Ezequiel Castellano, Ahmet Cetinkaya, Cédric Ho Thanh, Stefan Klikovits, Xiaoyi Zhang, and Paolo Arcaini, submitted the Frenetic tool to the CPS testing competition of SBST 2021 which showed good results in terms of diversity and failure generation rate.
The competition focused on the scenario generation for testing the lane-keeping capabilities of an autonomous driving system, using the BeamNG simulator. The goal was to generate virtual roads that lead to lane-keeping failures (a.k.a. OBEs, for Out-of-Bounds-Errors) within a certain time frame. The submitted tools competed in two distinct configurations, differing in time budget, vehicle configuration, and OBE tolerance.
Frenetic is a genetic approach that leverages a curvature-based road representation, and introduces some nuances aiming at improving diversity of the generated roads.
Implementations were not ranked, but the competition report (in print) states that Frenetic is generally “very effective and triggered many failures“. In fact, Frenetic was among the two tools that “triggered at least 10x more OBEs than the other tools for both configurations”. In particular, “Frenetic exposed the most diverse set of failures on average” and found ”the highest number of OBEs for which the ego-car invades the opposite traffic lane”, which might be of particular interest for car manufacturers.* The Frenetic implementation and a description of the algorithm are available in our repository.
Three papers (co)-authored by ERATO MMSD researchers accepted for presentation at ETAPS 2020 – European Joint Conferences on Theory and Practice of Software.
Juraj Kolčák, Jérémy Dubut, Ichiro Hasuo, Shin-Ya Katsumata, David Sprunger and Akihisa Yamada. Relational Differential Dynamic Logic. 26th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS 2020)
Takamasa Okudono and Andy King. Mind the Gap: Bit-vector Interpolation recast over Linear Integer Arithmetic. 26th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS 2020)
Satoshi Kura. Graded algebraic theories. 23rd International Conference on Foundations of Software Science and Computation Structures (FoSSaCS 2020)
The European Joint Conferences on Theory and Practice of Software (ETAPS) is the primary European forum for academic and industrial researchers working on topics relating to Software Science. ETAPS, established in 1998, is a confederation of four main annual conferences, ESOP, FASE, FOSSACS and TACAS, accompanied by satellite workshops and other events. ETAPS 2020 conference was planned in Dublin, Ireland, in April 2020 (postponed due to COVID-19).