WASABI: Whole-graph Assignment-based Stabilizer for lAne topology By Inter-frame tracking

2026-07-22 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

WASABI, a novel real-time post-processing pipeline, addresses structural instabilities in lane topology outputs from autonomous driving perception models. These models infer lane segments and their inter-lane connectivity (LCLC) from 360-degree BEV views, but often exhibit issues like missed detections, incorrect LCLC, over-detection, and label flicker. WASABI stabilizes these outputs both within and across frames by jointly tracking lane segments and their LCLC connectivity. Designed for onboard real-time constraints (10 Hz / 20 ms / up to 200 input lanes), the pipeline integrates segment tracking, connectivity, and noise-robust topology-aware refinement. On internal validation data across 16 sequences, WASABI improved LCLC detection F1 from 0.834 to 0.948 (+0.114, +13.6%) and reduced centerline lateral error from 2.50 m to 0.95 m. It also cut detection false-positives by 24.6%, LCLC toggle rate by 63.3%, and boundary-label flicker rate by 30.2%.

Key takeaway

For autonomous driving engineers developing robust lane perception systems, WASABI demonstrates that post-processing can significantly enhance stability and accuracy. You should consider implementing a real-time, whole-graph assignment-based stabilizer to reduce false positives, lateral errors, and temporal flicker. This approach improves LCLC detection F1 by 13.6% and cuts centerline lateral error by 62%, crucial for reliable navigation and safety.

Key insights

WASABI stabilizes autonomous driving lane topology perception by jointly tracking lane segments and their connectivity in real-time.

Principles

• Lane topology requires graph understanding.
• Jointly track segments and connectivity.
• Real-time processing is critical.

Method

WASABI employs a real-time post-processing pipeline integrating segment tracking with connectivity, noise-robust topology-aware refinement, and a resource-constrained design for onboard systems.

In practice

• Apply post-processing for perception stability.
• Consider joint tracking for graph structures.
• Prioritize real-time constraints in design.

Topics

• Autonomous Driving
• Lane Topology
• Perception Models
• Real-time Systems
• Post-processing
• Graph Tracking

Best for: Research Scientist, AI Scientist, Computer Vision Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.