Localization under Topological Uncertainty for Lane Identification of Autonomous Vehicles
This addresses the challenge of safe navigation for autonomous vehicles in dynamic road environments with uncertain map data, representing an incremental improvement in handling topological uncertainty.
The paper tackles the problem of autonomous vehicle localization when road topology in maps is inaccurate, by proposing a Variable Structure Multiple Hidden Markov Model (VSM-HMM) framework that simultaneously reasons about location and likely topologies, demonstrating its effectiveness for lane identification.
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.