Using Language and Road Manuals to Inform Map Reconstruction for Autonomous Driving

2025-06-12

AI Summary

This paper addresses the low accuracy and poor generalization of lane topology prediction in complex intersections for autonomous driving. We propose a lightweight enhancement method that jointly integrates linguistic semantics (e.g., road names) and structured priors (e.g., lane width specifications from roadway design manuals), explicitly encoding them alongside OpenStreetMap (OSM) metadata and SMERF online map priors to enable joint reasoning over linguistic rules and geometric constraints. Our approach incorporates centerline semantic enhancement and employs a topology-aware multi-metric evaluation framework comprising four complementary metrics. Experiments on two geographically diverse complex intersection benchmarks demonstrate significant improvements in lane and traffic element detection, association, and topology prediction accuracy. The results validate the robustness and cross-scene scalability of the proposed method.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Natural Language Processing: Lexical Semantics and Morphology
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Lane-topology prediction is a critical component of safe and reliable autonomous navigation. An accurate understanding of the road environment aids this task. We observe that this information often follows conventions encoded in natural language, through design codes that reflect the road structure and road names that capture the road functionality. We augment this information in a lightweight manner to SMERF, a map-prior-based online lane-topology prediction model, by combining structured road metadata from OSM maps and lane-width priors from Road design manuals with the road centerline encodings. We evaluate our method on two geo-diverse complex intersection scenarios. Our method shows improvement in both lane and traffic element detection and their association. We report results using four topology-aware metrics to comprehensively assess the model performance. These results demonstrate the ability of our approach to generalize and scale to diverse topologies and conditions.

Problem

• Predicting lane-topology for autonomous driving safety
• Enhancing road environment understanding using language and manuals
• Improving lane and traffic element detection accuracy

Innovation

• Combines OSM maps with road design manuals
• Augments SMERF model with road metadata
• Improves lane and traffic element detection