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

2026-07-22 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

WASABI, a Whole-graph Assignment-based Stabilizer for lAne topology By Inter-frame tracking, is a real-time post-processing pipeline designed to stabilize lane topology outputs for autonomous driving systems. Existing neural perception models often produce unstable outputs, including missed detections, incorrect inter-lane connectivity (LCLC), over-detection, and label flicker. WASABI addresses these issues by jointly tracking lane segments and their LCLC connectivity, operating under real-time constraints of 10 Hz, 20 ms, and up to 200 input lanes. The pipeline integrates segment tracking, connectivity, and noise-robust topology-aware refinement. On internal validation data comprising 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 decreased detection false-positives by 24.6%, LCLC toggle rate by 63.3%, and boundary-label flicker rate by 30.2%.

Key takeaway

For Computer Vision Engineers developing autonomous driving systems, if you are struggling with unstable lane topology outputs from neural perception models, WASABI demonstrates a robust post-processing solution. You should consider implementing joint tracking for lane segments and inter-lane connectivity to significantly reduce errors like false-positives, LCLC toggle rates, and lateral error, enhancing overall system reliability. This approach can improve perception F1 scores by over 13% and reduce flicker rates by 30-63%.

Key insights

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

Principles

• Lane topology requires joint segment and connectivity tracking.
• Real-time stabilization improves neural perception model reliability.
• Post-processing can significantly reduce perception errors.

Method

WASABI integrates segment tracking with inter-lane connectivity, applies noise-robust topology-aware refinement, and is designed for real-time operation (10 Hz / 20 ms / up to 200 input lanes).

In practice

• Apply post-processing to enhance lane topology stability.
• Use joint tracking for lane segments and connectivity.
• Reduce LCLC toggle rate and boundary-label flicker.

Topics

• Autonomous Driving
• Lane Topology
• Computer Vision
• Real-time Systems
• Perception Stabilization
• Inter-lane Connectivity

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 Computer Vision and Pattern Recognition.