English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

When traveling through a graph with an accessible deterministic path to atarget, is it ever preferable to resort to stochastic node-to-node transitionsinstead? And if so, what are the conditions guaranteeing that such a stochasticoptimal routing policy can be computed efficiently? We aim to answer thesequestions here by defining a class of Opportunistically Stochastic ShortestPath (OSSP) problems and deriving sufficient conditions for applicability ofnon-iterative label-setting methods. The usefulness of this framework isdemonstrated in two very different contexts: numerical analysis and autonomousvehicle routing. We use OSSPs to derive causality conditions forsemi-Lagrangian discretizations of anisotropic Hamilton-Jacobi equations. Wealso use a Dijkstra-like method to solve OSSPs optimizing the timing andurgency of lane change maneuvers for an autonomous vehicle navigating roadnetworks with a heterogeneous traffic load.

Monotone Causality in Opportunistically Stochastic Shortest Path Problems