The objective of this research is to present the state of the art of the safety assurance of
Artificial Intelligence (AI)-based systems and guidelines on future correlated work. For this …

Robust planning in interactive scenarios requires predicting the uncertain future to make risk-
aware decisions. Unfortunately, due to long-tail safety-critical events, the risk is often under …

This study applies novel risk measures, called Orlicz risks, to the risk and uncertainty
evaluation of the streamflow discharge as a primary driver of hydrological and hydraulic …

Uncertainty is prevalent in engineering design, statistical learning, and decision making
broadly. Due to inherent risk-averseness and ambiguity about assumptions, it is common to …

Motivated by the fragility of neural network (NN) controllers in safety-critical applications, we
present a data-driven framework for verifying the risk of stochastic dynamical systems with …

Safe navigation is a fundamental challenge in multi-robot systems due to the uncertainty
surrounding the future trajectory of the robots that act as obstacles for each other. In this …

The popularity of Conditional Value-at-Risk (CVaR), a risk functional from finance, has been
growing in the control systems community due to its intuitive interpretation and axiomatic …

In this survey, we design formal verification and control algorithms for autonomous systems
with practical safety guarantees using conformal prediction (CP), a statistical tool for …

The success of a multi-kilometre drive by a solar-powered rover at the lunar south pole
depends upon careful planning in space and time due to highly dynamic solar illumination …

This paper proposes a novel framework for identifying an agent's risk aversion using
interactive questioning. Our study is conducted in two scenarios: a one-period case and an …