位相不確実性下での自動運転車の車線識別のための自己位置推定
Localization under Topological Uncertainty for Lane Identification of Autonomous Vehicles
地図の位相構造が現実と一致しない状況に対応するため、動的な複数HMMを用いたVSM-HMMを提案し、自動運転車の車線推定に適用して有効性を示した。
著者: Samer B. Nashed, David M. Ilstrup, Joydeep Biswas
分類: cs.RO, cs.AI
原文アブストラクト
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.
