Localization under Topological Uncertainty for Lane Identification of Autonomous Vehicles

TL;DR

This paper introduces VSM-HMM, a novel framework for autonomous vehicle localization that effectively manages topological uncertainty, improving lane identification accuracy despite map inaccuracies and dynamic road conditions.

Contribution

The paper proposes the Variable Structure Multiple Hidden Markov Model (VSM-HMM) for robust topological localization in autonomous vehicles, addressing map inaccuracies and dynamic environments.

Findings

VSM-HMM outperforms traditional methods in topological localization accuracy.

The framework effectively handles discrepancies between map topology and real-world conditions.

An extended Earth Mover's Distance enhances belief distribution comparisons under uncertainty.

Abstract

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.