Brandes, Julian (University of Technology Nuremberg), Stuebler, Manuel (Esslingen University of Applied Sciences), Harr, Maximilian (Robert Bosch GmbH), Crocoll, Philipp (Robert Bosch GmbH), Burgard, Wolfram (University of Technology Nuremberg)

MRLGraph: A Novel, Graph-Based Map Relation Learning Network for Traffic Element to Lane Assignment Based on Geometric Map Data

Scheduled for presentation during the Regular Session "S37b-Reliable Perception and Robust Sensing for Intelligent Vehicles" (FR-EA-T37), Friday, November 21, 2025, 14:10−14:30, Coolangata 1

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Abstract

Precise understanding of lane-level road topology and the semantic relationships between traffic elements and lanes is crucial for highly autonomous driving systems, es- pecially for ensuring safe and compliant navigation through complex road networks. Current methods for lane graph generation primarily focus on geometric and structural aspects, largely neglecting the critical semantic associations to traffic lights and signs. To address this gap, we propose MRLGraph, a novel graph-based neural network method that explicitly learns and predicts the missing map relations using geomet- ric lane-level map information. Our approach utilizes graph attention mechanisms and path-based encoding of the lane segments to accurately capture both local geometric context and broader topological relationships. In extensive experiments, we demonstrate that our method significantly outperforms existing approaches and baseline models in terms of precision, recall, and generalization capabilities both on the existing Lyft and our own MRLScenes dataset. The latter is a novel hybrid dataset combining real-world high-definition map samples and diverse synthetic scenarios.