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---
license: mit
Modalities:
- image
- json
task_categories:
- question-answering
- summarization
language:
- en
tags:
- multimodal
- safety-critical scenario in AD
- Consistency-Centric Evaluation
- benchmarking
size_categories:
- n<1K
---
## RADIUS-Drive（及 RADIUS-Data）
本仓库在 Hugging Face 上发布 RADIUS-Drive（风险感知一致性诊断评测基准）。
This Hugging Face repo hosts RADIUS-Drive, a risk-aware diagnostic benchmark for safety-critical autonomous driving.
- GitHub: https://github.com/pupprtseven/RADIUS_Drive_Bench
- Paper:
## Benchmark Overview
RADIUS-Drive 关注 Pseudo-Correctness（伪正确性）：
模型可能给出表面正确的驾驶决策，但未能识别、定位或验证潜在风险机制。
RADIUS-Drive targets Pseudo-Correctness, where models produce correct-looking driving decisions while failing to trigger, localize, or validate underlying risk factors.
为此，RADIUS-Drive 提出 SAR（Safety → Awareness → Reasoning）诊断协议，
通过跨阶段一致性指标，系统性评估模型在安全关键决策中的 风险感知与推理一致性。
To this end, RADIUS-Drive introduces the SAR (Safety → Awareness → Reasoning) diagnostic protocol, enabling consistency-centric evaluation of risk perception and decision reasoning.
## 任务 / Tasks
### SAF（Safety）
检测模型是否能够识别并响应安全关键风险信号。
Detect whether the model correctly triggers safety awareness under risk-critical conditions.
### AWR（Awareness）
评估模型是否能够定位并解释风险来源，而非仅给出结果。
Assess whether the model localizes and explains the source of risk, beyond outcome correctness.
### REA（Reasoning）
验证模型的决策是否基于与风险一致的因果推理过程。
Verify whether decisions are supported by risk-consistent causal reasoning.
### X-CONS（Cross-Phase Consistency）
检查 Safety、Awareness 与 Reasoning 之间是否存在一致性违背（Pseudo-Correctness）。
Evaluate cross-phase consistency to diagnose pseudo-correct behavior.
## 关键约定 / Key Conventions
所有样本均配备 可审计的 ground truth，支持reference-based injection 与 reference-free generation 两种场景构造方式。
All samples provide auditable ground truth, supporting both reference-based injection and reference-free generation.
## 评测结果应同时报告：
阶段内性能（per-phase）
跨阶段一致性指标（e.g., CF-CDA, Guess）
Results should report both per-phase scores and cross-phase consistency metrics.
## Dataset Contents
Each instance **X** is released as an image plus JSON sidecars:
- `dataX.png`: the rendered driving scene.
- `dataX.json`: taxonomy and post-decision supervision, including:
- classification (Level-3)
- `lt_ele` (dominant element)
- `acc_factors`
- `post_dec` (reference level and/or plan text)
- `dataX_aligned.json`: simulator-ready coarse state for Phase-1 Safety (map/relations/kinematics abstraction).
- `dataX_gt.json`: pre-decision action tags for Phase-1 scoring (per-action collision/hazard/safe, and optional best action).
## Design Principles
The schema is intentionally minimal:
- `dataX_aligned.json` is only as complex as needed for deterministic Safety rollout.
- `dataX.json` carries the Phase-2/3 labels required for SAR diagnosis and cross-phase consistency metrics.
## Directory Structure
```
RADIUS_DataSet550/
├── json/ # JSON sidecars for each instance
├── pic/ # Rendered scene images
├── aligned_dataset.py # Utility script for alignment
├── example.json
├── example_plot.png
└── README.md
```
## Notes
- File naming follows the `dataX.*` convention across image and JSON sidecars.
- For benchmark usage, refer to the `RADIUS_benchmark` module documentation.
## License
MIT License |