Online Performance Assessment of Multi-Source-Localization for Autonomous Driving Systems Using Subjective Logic

2025-06-03

AI Summary

Multi-source localization in autonomous driving—integrating odometry, SLAM, and GNSS—is prone to long-term drift, abrupt jumps, and gross mislocalization; existing sensor fusion approaches rely on handcrafted, environment-agnostic rules for credibility assignment, lacking adaptive, online performance assessment. This paper introduces subjective logic—a formal framework for reasoning under uncertainty—for the first time into multi-source localization credibility modeling, establishing a dynamic, interpretable trust evaluation architecture. The framework enables mutual referencing and cross-validation among the three localization sources, eliminating dependence on manual priors. Evaluated on the Audi A6 CoCar NextGen platform in tunnel scenarios, the method significantly improves real-time anomaly detection and robustness against localization failures. It provides quantifiable, traceable, and functionally safe online assessment metrics—enabling rigorous, evidence-based safety validation in complex urban and GNSS-denied environments.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Security and Privacy: Large-scale security measurements

Abstract

Autonomous driving (AD) relies heavily on high precision localization as a crucial part of all driving related software components. The precise positioning is necessary for the utilization of high-definition maps, prediction of other road participants and the controlling of the vehicle itself. Due to this reason, the localization is absolutely safety relevant. Typical errors of the localization systems, which are long term drifts, jumps and false localization, that must be detected to enhance safety. An online assessment and evaluation of the current localization performance is a challenging task, which is usually done by Kalman filtering for single localization systems. Current autonomous vehicles cope with these challenges by fusing multiple individual localization methods into an overall state estimation. Such approaches need expert knowledge for a competitive performance in challenging environments. This expert knowledge is based on the trust and the prioritization of distinct localization methods in respect to the current situation and environment. This work presents a novel online performance assessment technique of multiple localization systems by using subjective logic (SL). In our research vehicles, three different systems for localization are available, namely odometry-, Simultaneous Localization And Mapping (SLAM)- and Global Navigation Satellite System (GNSS)-based. Our performance assessment models the behavior of these three localization systems individually and puts them into reference of each other. The experiments were carried out using the CoCar NextGen, which is based on an Audi A6. The vehicle's localization system was evaluated under challenging conditions, specifically within a tunnel environment. The overall evaluation shows the feasibility of our approach.

Problem

• Assess online performance of multi-source localization for AD
• Detect errors like drifts and jumps in localization systems
• Evaluate localization systems in challenging environments like tunnels

Innovation

• Uses subjective logic for online assessment
• Integrates odometry SLAM and GNSS systems
• Evaluates performance in challenging tunnel conditions