Fast Option Ranking in Autonomous Systems for Criticality Evasion under Uncertainties

Bineet Ghosh, Parasara Sridhar Duggirala, Samarjit Chakraborty

Abstract: We study the problem where an autonomous system is in a critical situation and is faced with multiple options among which it has to choose to safely evade the criticality. Each of these options is also associated with some uncertainty. Traditional approaches from formal methods require a reachability analysis to evaluate which of the options is safe. While the computational cost of reachability analysis is well known, the presence of uncertainty adds an additional layer of complexity. As a result, performing reachability analysis for all the options before choosing one will not be feasible due to time constraints. This is a practical problem that arises is various scenarios, such as an autonomous vehicle in a potential accident that it has to evade to minimize damage. While models and algorithms for reachability analysis have been widely studied, reachability analysis in the presence of uncertainties have been less so. Despite its many applications, to the best of our knowledge, the problem of choosing in real-time, one of the many options for criticality evasion has not been studied in the past. We address this problem by proposing a new real-time reachable set computation technique for uncertain linear systems using techniques from perturbation theory.

External IDs:dblp:conf/fdl/GhoshDC25