ISU-Test/isu-challenge-dataset

Dataset Card for ISU Challenge Dataset

Dataset Summary

ISU Challenge Dataset is a synthetic, multi-modal in-cabin automotive dataset.

The dataset contains 1000 synchronized samples with:

• RGB render
• depth (EXR and PNG)
• instance segmentation
• canny edge map
• structured scenario labels

Each sample is linked through a manifest entry and shares the same sample index and name across modalities.

Supported Tasks

This dataset can support:

• Semantic segmentation using instance segmentation masks.
• Multi-label classification for cabin state/safety/context variables from labels JSON.
• Depth estimation benchmarking with paired RGB and depth.
• Edge-focused pretraining/evaluation using canny maps.

Languages

No natural-language corpus is included. Label keys and categorical values are English identifiers.

Dataset Structure

Top-level layout:

• canny/
• depth/exr/
• depth/png/
• images/
• instance_seg/
• labels/
• manifest.json

Note: manifest records retain json_path values from generation time (for traceability). In this packaged release, per-sample structured annotations are provided under labels/.

Data Instances

A typical manifest record:

{
    "sample_index": 0,
    "sample_name": "sample_00000",
    "json_path": "json_inputs/sample_00000.json",
    "image_path": "images/sample_00000_sim.png",
    "depth_path": "depth/exr/sample_00000_depth.exr",
    "instance_seg_path": "instance_seg/sample_00000_instance_seg.png",
    "canny_path": "canny/sample_00000_canny.png",
    "params": {
        "env": "HIGHWAY",
        "driver_gender": "FEMALE",
        "driver_safety_belt": "NO",
        "passenger_codriver": "NO",
        "suitcase": "YES",
        "driver_height_m": 1.96,
        "driver_weight_kg": 83.4,
        "light_front": 0.31,
        "env_strength": 0.92
    }
}

Data Fields

• sample_index (int): Unique sample id in [0, 999].
• sample_name (string): Canonical id, format sample_XXXXX.
• json_path (string): Path to scene label.
• image_path (string): Path to RGB rendered image.
• depth_path (string): Path to EXR depth map.
• instance_seg_path (string): Path to instance segmentation mask.
• canny_path (string): Path to canny edge image.
• params (object): Structured labels/conditions for the scene, including environment, occupants, safety-belt states, props, and continuous lighting/photometric parameters.

Data Splits

This release contains a single split (train-like full set).

Split	Samples
all	1000

Dataset Creation

Curation Rationale

The dataset was created to support repeatable, controlled in-cabin perception experiments where scene factors can be explicitly configured and audited.

Source Data

Initial Data Collection and Normalization

Data is fully synthetic and generated from structured scenario configurations. The finalized run used Blender with following settings:

• Renderer: Cycles
• Resolution: 960x540
• Seed: 42

Annotations

Annotation process

Annotations are programmatic and generated by the simulation pipeline (no manual annotation round). Labels are stored as JSON per sample and mirrored in manifest params.

Personal and Sensitive Information

This dataset is synthetic. It does not contain real-person photos or directly identifying personal data.

Considerations for Using the Data

Social Impact of Dataset

• Faster iteration for in-cabin vision models in safety-related scenarios.
• Reproducible controlled testing of feature interactions.

Discussion of Biases

Bias may arise from design choices in:

• Scenario parameter ranges.
• Asset library and occupancy patterns.
• Lighting/environment priors.

Additional Information

Dataset Curators

Lev Sorokin, Rifaath Ameen, Stefano Carlo Lambertenghi, Chen Yang.

Licensing Information

MIT License.

Citation Information

If you use this dataset, cite the following:

@inproceedings{sorokin2026isutest,
  author    = {Lev Sorokin and Chen Yang and Ken E. Friedl and Andrea Stocco},
  title     = {Search-based Testing of Vision Language Models for In-Car Scene Understanding},
  booktitle = {Proceedings of the 41st IEEE/ACM International Conference on Automated Software Engineering (ASE 2026), Industry Track},
  year      = {2026},
  doi       = {10.1145/3832783.3834506}
}