# Coinfra Sync Eval

> Evaluates the temporal synchronization accuracy and robustness of a multi-node cooperative perception system under adverse weather and network conditions. It measures how well a delay-aware protocol aligns sensor data across nodes compared to naive asynchronous methods, focusing on timing errors, fusion completeness, and reaction latency. Use when the user wants to benchmark on CoInfra, or asks about evaluating this task. Reports full_match_rate.

- Skill: `qhjqhj00/coinfra-sync-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/coinfra-sync-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/coinfra-sync-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/coinfra-sync-eval

---


# coinfra-sync-eval

> CoInfra: A Large-Scale Cooperative Infrastructure Perception System and Dataset in Adverse Weather — Minghao Ning et al. (2025) (arXiv:2507.02245, 2025)

## What this evaluates

Evaluates the temporal synchronization accuracy and robustness of a multi-node cooperative perception system under adverse weather and network conditions. It measures how well a delay-aware protocol aligns sensor data across nodes compared to naive asynchronous methods, focusing on timing errors, fusion completeness, and reaction latency.

## Datasets

- **CoInfra** — total ?; splits: test (-1); repo https://github.com/NingMingHao/CoInfra

## Metrics

- `full_match_rate` **(primary)** — range: [0, 1]
  - Percentage of synchronization cycles wherein data from all nodes successfully align to the global anchor. Computed as (number of fully aligned cycles / total cycles) * 100.
- `reaction_time` — range: other
  - Latency from the global anchor timestamp to the actual fusion trigger event timestamp. Measured in milliseconds.
- `timing_error` — range: other
  - Time difference between an individual node's data acquisition timestamp and its corresponding global time anchor. Measured in milliseconds.
- `min_max_delay` — range: other
  - Time difference between the earliest and latest data acquisitions among all nodes for each global trigger event. Measured in milliseconds.

## Input / output format

**Input**: Timestamps of sensor data acquisitions from N nodes, global trigger schedule, and simulated/real network latency distributions (e.g., Gaussian delays, message drop rates).

**Output**: Per-cycle alignment status (aligned/misaligned), fusion trigger timestamps, and aggregated delay/error statistics across all synchronization cycles.

## Scoring recipe

```python
for each global trigger event:
  timestamps = [node_acq_time for node in nodes]
  timing_errors = [abs(t - global_anchor) for t in timestamps]
  min_max_delay = max(timestamps) - min(timestamps)
  full_match = 1 if all(t within window) else 0
  reaction_time = fusion_trigger_time - global_anchor

aggregate over all cycles:
  full_match_rate = sum(full_match) / total_cycles
  mean_timing_error = mean(timing_errors)
  mean_reaction_time = mean(reaction_time)
  mean_min_max_delay = mean(min_max_delay)
```

## Common pitfalls

- Confusing the adaptive fusion window parameter (N sigma) with a measured metric; N sigma is a tunable configuration, not an outcome.
- Assuming late-arriving messages are discarded; the protocol explicitly retains them with reduced confidence or integrates them via post-fusion updates.
- Treating timing_error and min_max_delay as identical; timing_error measures per-node alignment to the anchor, while min_max_delay measures inter-node spread.

## Evidence (verbatim from paper)

> full match rate, defined as the percentage of synchronization cycles wherein data from all nodes successfully align to the global anchor; and reaction time, defined as the latency from the global anchor to the fusion trigger event.

## Citation

```bibtex
@misc{ning2025coinfra,
  title={CoInfra: A Large-Scale Cooperative Infrastructure Perception System and Dataset in Adverse Weather},
  author={Minghao Ning et al. (2025)},
  year={2025},
  note={arXiv:2507.02245}
}
```

- arXiv: 2507.02245