TopoStreamer: Temporal Lane Segment Topology Reasoning in Autonomous Driving

2025-07-01

AI Summary

To address inaccurate lane topology inference in autonomous driving—caused by inconsistent positional embeddings and insufficient modeling of temporal multi-attribute dynamics in road network reconstruction—this paper proposes an end-to-end temporal-aware model. Methodologically, it introduces three key innovations: (1) a streaming attribute constraint to enforce structural consistency; (2) dynamic lane-boundary positional encoding for precise real-time spatial awareness; and (3) a lane-segment denoising mechanism to enhance spatiotemporal coherence. Furthermore, it incorporates multi-attribute temporal learning and query optimization, along with a dedicated lane-boundary classification metric for fine-grained topological evaluation. Experimental results on OpenLane-V2 demonstrate significant improvements: +3.4% mAP for lane segments and +2.1% OLS (Ordered Lane Segment) for centerlines, outperforming state-of-the-art methods.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Lane segment topology reasoning constructs a comprehensive road network by capturing the topological relationships between lane segments and their semantic types. This enables end-to-end autonomous driving systems to perform road-dependent maneuvers such as turning and lane changing. However, the limitations in consistent positional embedding and temporal multiple attribute learning in existing methods hinder accurate roadnet reconstruction. To address these issues, we propose TopoStreamer, an end-to-end temporal perception model for lane segment topology reasoning. Specifically, TopoStreamer introduces three key improvements: streaming attribute constraints, dynamic lane boundary positional encoding, and lane segment denoising. The streaming attribute constraints enforce temporal consistency in both centerline and boundary coordinates, along with their classifications. Meanwhile, dynamic lane boundary positional encoding enhances the learning of up-to-date positional information within queries, while lane segment denoising helps capture diverse lane segment patterns, ultimately improving model performance. Additionally, we assess the accuracy of existing models using a lane boundary classification metric, which serves as a crucial measure for lane-changing scenarios in autonomous driving. On the OpenLane-V2 dataset, TopoStreamer demonstrates significant improvements over state-of-the-art methods, achieving substantial performance gains of +3.4% mAP in lane segment perception and +2.1% OLS in centerline perception tasks.

Problem

• Improving lane segment topology reasoning for autonomous driving
• Addressing inconsistent positional embedding in roadnet reconstruction
• Enhancing temporal multiple attribute learning accuracy

Innovation

• Streaming attribute constraints for temporal consistency
• Dynamic lane boundary positional encoding
• Lane segment denoising for pattern diversity