Multi-Sensor Fusion Method using Dynamic Bayesian Network for Precise Vehicle Localization and Road Matching

TL;DR

This paper introduces a multi-sensor fusion approach using a Dynamic Bayesian Network to improve vehicle localization and road matching accuracy in real-time driving assistance systems, especially in ambiguous scenarios.

Contribution

It proposes a novel multi-sensor fusion strategy with a Dynamic Bayesian Network for enhanced road matching and vehicle localization in real-time navigation.

Findings

Effective in ambiguous situations

Utilizes ABS, GPS, and digital maps data

Improves real-time vehicle localization

Abstract

This paper presents a multi-sensor fusion strategy for a novel road-matching method designed to support real-time navigational features within advanced driving-assistance systems. Managing multihypotheses is a useful strategy for the road-matching problem. The multi-sensor fusion and multi-modal estimation are realized using Dynamical Bayesian Network. Experimental results, using data from Antilock Braking System (ABS) sensors, a differential Global Positioning System (GPS) receiver and an accurate digital roadmap, illustrate the performances of this approach, especially in ambiguous situations.