Fail-Safe Multi-Modal Localization Framework Using Heterogeneous Map-Matching Sources

A highly accurate and robust real-time localization process is crucial for autonomous driving applications. Numerous methods for localization have been proposed, which combine various kinds of input, such as data from environmental sensors, inertial measurement units (IMU), and the Global Positioning System (GPS). Because reliance on a single environmental sensor is a vulnerable approach, the use of multiple environmental sensors is a better alternative. However, the fusion methods from previous studies have not adequately compensated for the drawbacks due to the lack of sensor diversity nor have the methods considered the fail-safe issue. In this paper, we propose a multi-modal fusion-based localization framework that uses multiple map matching sources. The framework contains two independent map matching sources and integrates them in a stochastic situational analysis model. By applying a probabilistic model, the more reliable map matching between the multiple sources is determined and the system stability is verified via a fail-safe action. A number of experiments with autonomous vehicles within actual driving environments have shown that combining multiple map matching sources yield more robust results than the use of a single map matching.

Automated summary

This paper proposes a multi-modal fusion-based localization framework that uses multiple map matching sources to achieve highly accurate and robust real-time localization. Experiments have shown that combining multiple map matching sources yields more reliable results compared to using a single map matching.