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Research report · Planned

Real-Time Driving Scene Segmentation

Planned perception benchmark · repository coming soon

This planned project will avoid generic vision claims by focusing on metrics and failure cases that affect autonomous driving and intelligent mobility safety.

Repository coming soonResearch map

Abstract

Create a real-time semantic segmentation baseline for driving scenes with explicit reporting of latency, class-wise errors, and safety-relevant failure cases.

Scientific question: How can segmentation quality and latency be reported in a way that matters for autonomous driving safety?

Contribution: Planned reproducible segmentation benchmark with safety-oriented reporting.

Marked research artifact slot

Real-time perception architecture

Architecture figure slot for verified diagrams of modules, data flow, and software boundaries.

Synthetic / planned visual

Marked research artifact slot

Frame · model · safety-oriented report

Pipeline slot for GIFs or animations that explain estimation, planning, perception, or evaluation steps.

Synthetic / planned visual

Marked research artifact slot

Synthetic driving-scene overlay

Demo slot reserved for real project videos or synthetic demos that are explicitly labeled as synthetic.

Synthetic / planned visual

Research questions

  • Which classes create safety-critical segmentation errors?
  • What latency-quality tradeoff appears for lightweight models?
  • How can uncertainty overlays make failure cases more transparent?

Methods

Real-time segmentationLatency profilingClass-wise analysisQualitative failure gallery

Implementation status

Implemented

  • Problem framing

Research prototype

  • Synthetic overlay placeholder

Planned

  • Dataset selection
  • Baseline model
  • Latency measurements
  • Failure-case report

Experiments and metrics

Experiments

  • Planned segmentation benchmark

Metrics

  • mIoU — planned
  • FPS — planned
  • class-wise error — planned

Quantitative benchmark tables will be added only after reproducible experiments are available.

Limitations

  • No validated model results are claimed yet.
  • Quantitative claims are intentionally withheld until reproducible benchmark runs are available.
  • Visual figures and GIFs are placeholders unless a project page explicitly states that an artifact is generated from real experiments.

Reproducibility plan

Experiment workflow

Each module is prepared to document configuration files, datasets, evaluation metrics, and repeated experiment runs before quantitative claims are shown.

Repository setup

Public repositories are linked when available. Private, planned, or incomplete repositories remain clearly labelled rather than being presented as finished systems.

Literature context

  • Semantic segmentation
  • Autonomous driving perception
  • Real-time vision

Roadmap

  • Create repository
  • Add reproducible inference scripts
  • Connect outputs to SafeCrossAI scene understanding