The KITScenes dataset is provided to you under a Creative Commons Attribution-NonCommercial 4.0 International Public License (CC BY-NC 4.0), with the additional terms included herein. When you download or use the dataset, you are agreeing to comply with the terms of CC BY-NC 4.0 as applicable, and also agreeing to the dataset terms (listed below). Where these dataset terms conflict with the terms of CC BY-NC 4.0, these dataset terms shall prevail.

Dataset terms:

In case you use the dataset within your research papers, you refer to our respective publication. If the dataset is used in media, a link to our websites (kitscenes.com) is included.
We take steps to protect the privacy of individuals by anonymizing faces and license plates using state-of-the-art anonymization software from BrighterAI. To the extent that you like to request removal of specific images/data frames from the dataset, please contact info@mrt.kit.edu.
We reserve all rights that are not explicitly granted to you. The dataset is provided as is, and you take full responsibility for any risk of using it.

KITScenes Multimodal

A high-fidelity sensor suite and the most complete HD maps of any public autonomous driving dataset.

Links: Dataset website · Python API on GitHub · Download on HuggingFace

Early release. KITScenes Multimodal is published at version 1.0.x. The on-disk schema is in place, but files, annotations, splits, and documentation may still change. For final benchmark reporting, please wait for a more stable public release.

Reprojection of HD map labels into 6 of the 9 cameras, with LiDAR points reprojected into the rear cameras.

KITScenes Multimodal is a European urban autonomous driving dataset targeting Level 4 robotaxi requirements, recorded across Karlsruhe, Frankfurt, and Sindelfingen by the Institute of Measurement and Control Systems (MRT) at Karlsruhe Institute of Technology (KIT). The dataset is built around a high-fidelity sensor suite: nine high-resolution global-shutter cameras provide full 360° surround coverage at 72.5 MPix per frame, enabling novel view synthesis and holistic HD map perception, while seven highly dense long-range LiDARs with an effective range beyond 400 m push the limits of what current perception methods can achieve.

KITScenes Multimodal provides what we believe to be the most complete HD maps of any public autonomous driving dataset, annotated in Lanelet2 format across 62 km² with full topological connectivity between lanes, signs, and traffic lights. The maps have been validated in closed-loop autonomous driving trials using the open-source Autoware stack — meaning they are ready to drive on directly, while simultaneously enabling research to close the gap between the current state of the art and the actual requirements of L4 robotaxi deployment.


1,000+ driving scenarios	72.5 MPix per frame · global shutter	400+ m effective LiDAR range	62 km² Lanelet2 HD maps

Highlights

European urban focus — recordings from Karlsruhe, Frankfurt, and Sindelfingen
High-fidelity sensor suite — 72.5 MPix global-shutter surround cameras, 7 LiDARs (~906k points/frame on average), 3 Continental ARS548 4D imaging radars, and redundant GNSS/INS
Long-range sensing — effective LiDAR range beyond 400 m with substantially higher return density than common public driving datasets
Production-grade HD maps — Lanelet2 maps with lane topology, regulatory elements, 29 road-feature classes, 120 traffic-sign classes (GTSIGN-220 taxonomy), and 3D-localized signs, traffic lights, and poles
Research benchmarks — relational HD map perception, long-range monocular depth estimation, novel view synthesis, and end-to-end / world-model research

Python API

Install the kitscenes Python package from GitHub for dataset loading, sensor access, map queries, visualization, and download helpers. See the API README for setup, download options, and example notebooks.

Versioning

This dataset follows Semantic Versioning (MAJOR.MINOR.PATCH) for data and on-disk layout:

Bump	Version	Meaning
Major	`X.0.0`	New dataset schema or file format — breaks backwards compatibility
Minor	`1.X.0`	New driving data — additional scenarios or recordings
Patch	`1.0.X`	Updated maps, labels, metadata, or calibrations within the existing schema — fully compatible, no new scenarios

Each release is tagged on HuggingFace (e.g. v1.0.1) so experiments remain reproducible. Pin a tag or commit when downloading for benchmark work.

The companion Python API on GitHub uses semver for code separately. It is currently at 0.1.x (alpha pre-release) and may change without notice.

Release notes — dataset

v1.0.1

small map update

v1.0.0

Initial early release on HuggingFace
Established data layout, splits, and release structure

About this early release

This repository provides a preview of KITScenes Multimodal at version 1.0.x. During this stage, files, annotations, split definitions, and documentation may be refined without notice. If you need a fixed benchmark snapshot, pin a HuggingFace release tag. For a more stable public release, please check back later.

Intended use

KITScenes Multimodal is intended for academic research on autonomous driving perception, mapping, spatial learning, neural rendering, and embodied AI. In its current early-release form, it is best suited for early exploration, pipeline integration, and preview experiments rather than final benchmark reporting.

Access and license

Access is gated. By requesting access, you acknowledge the dataset terms listed above and agree to use the data under CC BY-NC 4.0 together with the additional KITScenes terms.

Citation

If you use KITScenes Multimodal in research, please cite:

The Road Ahead in Autonomous Driving: The KITScenes Multimodal Dataset
arXiv:2606.02956 · Dataset website

@misc{schwarzkopf2026kitscenes,
      title={The Road Ahead in Autonomous Driving: The KITScenes Multimodal Dataset}, 
      author={Richard Schwarzkopf and Fabian Immel and Alexander Blumberg and Jonas Merkert and Nils Rack and Kaiwen Wang and Fabian Konstantinidis and Julian Truetsch and Carlos Fernandez and Annika Bätz and Kevin Rösch and Marlon Steiner and Willi Poh and Yinzhe Shen and Royden Wagner and Felix Hauser and Dominik Strutz and Jaime Villa and Gleb Stepanov and Holger Caesar and Ömer Şahin Taş and Frank Bieder and Jan-Hendrik Pauls and Christoph Stiller},
      year={2026},
      eprint={2606.02956},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2606.02956}, 
}

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Paper for KIT-MRT/KITScenes-Multimodal

The Road Ahead in Autonomous Driving: The KITScenes Multimodal Dataset

Paper • 2606.02956 • Published 10 days ago • 17