Toward Localization-Based Automated Driving in Highly Dynamic Environments: Comparison and Discussion of Observation Models

Abstract

To robustly localize the pose of an ego vehicle within a dynamic environment, it is important to model the sensor measurements precisely, including changes in the environment. This study describes the observation models developed for localization performed in highly dynamic environments, and presents the results of comparing these models. In this study, four observation models, including our previously proposed model, were compared by conducting a simulation. The models had different ways of coping with changes in the environment, and produced different results. Moreover, the comparison results revealed that each model had its own advantages and disadvantages. Finally, we demonstrated that our previously proposed model can achieve satisfactory performance in terms of computation complexity and estimation accuracy.