1 research outputs found
Localization under Topological Uncertainty for Lane Identification of Autonomous Vehicles
Autonomous vehicles (AVs) require accurate metric and topological location
estimates for safe, effective navigation and decision-making. Although many
high-definition (HD) roadmaps exist, they are not always accurate since public
roads are dynamic, shaped unpredictably by both human activity and nature.
Thus, AVs must be able to handle situations in which the topology specified by
the map does not agree with reality. We present the Variable Structure Multiple
Hidden Markov Model (VSM-HMM) as a framework for localizing in the presence of
topological uncertainty, and demonstrate its effectiveness on an AV where lane
membership is modeled as a topological localization process. VSM-HMMs use a
dynamic set of HMMs to simultaneously reason about location within a set of
most likely current topologies and therefore may also be applied to topological
structure estimation as well as AV lane estimation. In addition, we present an
extension to the Earth Mover's Distance which allows uncertainty to be taken
into account when computing the distance between belief distributions on
simplices of arbitrary relative sizes.Comment: 6 pages, to appear in ICRA 201