| | --- |
| | 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 |
