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Dataset

Lead Carla

by ln2697 hf-dataset--ln2697--lead_carla
Nexus Index
30.1 Top 100%
S: Semantic 50
A: Authority 0
P: Popularity 47
R: Recency 59
Q: Quality 30
Tech Context
Vital Performance
0 DL / 30D
0.0%
Data Integrity 30.1 FNI Score
- Size
- Rows
Parquet Format
- Tokens
Dataset Information Summary
Entity Passport
Registry ID hf-dataset--ln2697--lead_carla
License MIT
Provider huggingface
πŸ“œ

Cite this dataset

Academic & Research Attribution

BibTeX
@misc{hf_dataset__ln2697__lead_carla,
  author = {ln2697},
  title = {Lead Carla Dataset},
  year = {2026},
  howpublished = {\url{https://huggingface.co/datasets/ln2697/lead_carla}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
ln2697. (2026). Lead Carla [Dataset]. Free2AITools. https://huggingface.co/datasets/ln2697/lead_carla

πŸ”¬Technical Deep Dive

Full Specifications [+]

βš–οΈ Nexus Index V2.0

30.1
TOP 100% SYSTEM IMPACT
Semantic (S) 50
Authority (A) 0
Popularity (P) 47
Recency (R) 59
Quality (Q) 30

πŸ’¬ Index Insight

FNI V2.0 for Lead Carla: Semantic (S:50), Authority (A:0), Popularity (P:47), Recency (R:59), Quality (Q:30).

Free2AITools Nexus Index

Verification Authority

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Downloads
16,512

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Dataset Specification

LEAD: Minimizing Learner–Expert Asymmetry in End-to-End Driving

Project Page | Paper | Code

Official CARLA dataset accompanies our paper LEAD: Minimizing Learner–Expert Asymmetry in End-to-End Driving.

We release the complete pipeline required to achieve state-of-the-art closed-loop performance on the Bench2Drive benchmark. Built around the CARLA simulator, the stack features a data-centric design with:

  • Extensive visualization suite and runtime type validation for easier debugging.
  • Optimized storage format, packs 72 hours of driving in ~200GB.
  • Native support for NAVSIM and Waymo Vision-based E2E and extending those benchmarks through closed-loop simulation and synthetic data for additional supervision during training.

Find more information on https://github.com/autonomousvision/lead.

Format

Each route is stored as a sequence of synchronized frames. All sensor modalities are ego-centric and time-aligned. In addition to the nominal sensor suite, we provide a second, perturbated sensor stack corresponding to a counterfactual ego state used for recovery supervision.

html
β”œβ”€β”€ bboxes/                  # Per-frame 3D bounding boxes for all actors
β”œβ”€β”€ depth/                   # Compressed depth maps (should be used for auxiliary supervision only)
β”œβ”€β”€ depth_perturbated        # Depth from a perturbated ego state
β”œβ”€β”€ hdmap/                   # Ego-centric rasterized HD map
β”œβ”€β”€ hdmap_perturbated        # HD map aligned to perturbated ego pose
β”œβ”€β”€ lidar/                   # LiDAR point clouds
β”œβ”€β”€ metas/                   # Per-frame metadata and ego state
β”œβ”€β”€ radar/                   # Radar detections
β”œβ”€β”€ radar_perturbated        # Radar detections from perturbated ego state
β”œβ”€β”€ rgb/                     # Front-facing RGB images
β”œβ”€β”€ rgb_perturbated          # RGB images from perturbated ego state
β”œβ”€β”€ semantics/               # Semantic segmentation maps
β”œβ”€β”€ semantics_perturbated    # Semantics from perturbated ego state
└── results.json             # Route-level summary and evaluation metadata

Download

You can either download a single route (useful for quick inspection / debugging) or clone the full dataset via Git LFS and unzip all routes.

Note: Download the dataset after setting up the lead repository.

Option 1: Download a single route

bash
bash scripts/download_one_route.sh

Option 2: Download all routes (Git LFS)

Clone the dataset repository directly into the expected directory:

bash
git lfs install
git clone https://huggingface.co/datasets/ln2697/lead_carla data/carla_leaderboard2/zip

Unzip routes

Run

bash
bash scripts/unzip_routes.sh

Citation

If you find this work useful, please cite:

bibtex
@article{Nguyen2025ARXIV,
  title={LEAD: Minimizing Learner-Expert Asymmetry in End-to-End Driving},
  author={Nguyen, Long and Fauth, Micha and Jaeger, Bernhard and Dauner, Daniel and Igl, Maximilian and Geiger, Andreas and Chitta, Kashyap},
  journal={arXiv preprint arXiv:2512.20563},
  year={2025}
}

License

This project is released under the MIT License

Social Proof

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πŸ›‘οΈ Dataset Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

πŸ†” Identity & Source

id
hf-dataset--ln2697--lead_carla
slug
ln2697--lead_carla
source
huggingface
author
ln2697
license
MIT
tags
license:mit, size_categories:1m<n<10m, arxiv:2512.20563, region:us, autonomous-driving, imitation-learning, carla, transfuser

βš™οΈ Technical Specs

architecture
null
params billions
null
context length
null
pipeline tag

πŸ“Š Engagement & Metrics

downloads
16,512
stars
2
forks
0

Data indexed from public sources. Updated daily.