Datasets:

Voxel51
/

HighwayScene

license: cc-by-nc-sa-4.0
task_categories:
  - other
tags:
  - fiftyone
  - multimodal
  - lidar
  - roadside-perception
  - background-subtraction
  - intelligent-transportation-systems
  - point-cloud
  - mcap
pretty_name: HighwayScene
size_categories:
  - n<1K
dataset_summary: >
  HighwayScene is a synchronized multi-LiDAR roadside dataset recorded at a
  highway construction site. Three statically mounted sensors — an Ouster OS0
  (rotating ToF, 10 Hz), an Aeva Aeries II (FMCW, 10 Hz), and a Blickfeld QB2
  (solid-state ToF, 5 Hz) — observe the same free-flowing highway traffic scene
  simultaneously. This FiftyOne version re-encodes the original protobuf records
  as MCAP episodes for exploration in the FiftyOne multimodal viewer.


  The dataset contains 30 episodes (~20 seconds each), split into train (20),
  val (5), and test (5). Each episode carries three synchronized point-cloud
  streams with per-point fields including radial velocity (Aeva) and beam
  identifiers (Ouster, Blickfeld).


  ## Installation


  ```bash

  pip install -U fiftyone

  ```


  ## Usage


  ```python

  import fiftyone as fo

  from fiftyone.utils.huggingface import load_from_hub


  dataset = load_from_hub("Voxel51/HighwayScene")

  session = fo.launch_app(dataset)

  ```

Dataset Card for HighwayScene

Installation

pip install -U fiftyone

Usage

import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub

dataset = load_from_hub("Voxel51/HighwayScene")
session = fo.launch_app(dataset)

Dataset Details

Dataset Description

HighwayScene is a synchronized multi-LiDAR roadside dataset introduced for the cross-sensor background subtraction benchmark in "Beam-Wise Statistical Background Subtraction for Static Roadside LiDAR: A Cross-Sensor Benchmark Study" (ITSC 2026). Three statically mounted sensors with different sensing principles observe the same highway construction-site scene simultaneously:

Sensor	Principle	Rate
Ouster OS0	Rotating time-of-flight	10 Hz
Aeva Aeries II	FMCW	10 Hz
Blickfeld QB2	Solid-state time-of-flight	5 Hz

The scene features consistently high traffic volume under a legally enforced 40 km/h speed limit. The dataset supports research on background modeling, dynamic-point segmentation, and cross-sensor benchmarking for static roadside LiDAR.

This FiftyOne version converts the original protobuf .pb records to MCAP format so each episode can be explored interactively in the FiftyOne multimodal viewer, with all three sensor streams on a shared 20-second timeline.

Curated by: Alexander Baumann, Marcel Voßhans, Thao Dang — Institute for Intelligent Systems, Esslingen University of Applied Sciences, Germany
Funded by: [More Information Needed]
Shared by: Institute for Intelligent Systems (IIS), Esslingen University of Applied Sciences
Language(s): N/A
License: CC BY-NC-SA 4.0 (non-commercial use only)

Dataset Sources

Repository: https://huggingface.co/datasets/iis-esslingen/HighwayScene (original protobuf records)
Paper: https://arxiv.org/abs/2608.14868
Demo: https://highwayscene.github.io/

Uses

Direct Use

Benchmarking beam-wise statistical background subtraction methods across heterogeneous LiDAR technologies
Cross-sensor evaluation of static/dynamic point-cloud segmentation
Research on roadside LiDAR perception: traffic monitoring, infrastructure-based sensing, inter-vehicle distance estimation
Multi-modal LiDAR data exploration via the FiftyOne App's 3D tile and shared timeline

Out-of-Scope Use

Moving-sensor or ego-vehicle applications — the benchmark assumes a statically mounted sensor with a fixed scan pattern
Commercial use — the CC BY-NC-SA 4.0 license prohibits commercial applications
Scenes with substantial long-term structural changes, changed sensor poses, or dynamically varying scan patterns

Dataset Structure

FiftyOne Dataset Topology

Media type: multimodal (MCAP files) Sample count: 30 episodes Splits: train (20), val (5), test (5), encoded as the split string field and queryable via dataset.match(fo.ViewField("split") == "test")

Each FiftyOne sample points to one .mcap file covering approximately 20 seconds of synchronized multi-sensor recording. The MCAP file contains three point-cloud topics (/ouster/points, /aeva/points, /blickfeld/points) and three static identity frame transforms on /tf_static. All topics use the foxglove.PointCloud schema and are rendered in the FiftyOne App's 3D tile on a shared timeline.

Sample Fields

Field	FiftyOne type	Description
`filepath`	`StringField`	Absolute path to the `.mcap` episode file
`split`	`StringField`	Benchmark split: `"train"`, `"val"`, or `"test"`
`record_name`	`StringField`	Original `.pb` filename stem (encodes recording date and global frame-ID range)
`first_frame_id`	`IntField`	First global frame ID in this episode (1-based, dataset-wide)
`last_frame_id`	`IntField`	Last global frame ID in this episode
`duration_s`	`FloatField`	Episode duration in seconds (~19.9 s for 200-frame episodes)
`message_count`	`IntField`	Total MCAP message count across all topics in this episode
`ouster_frames`	`IntField`	Number of Ouster OS0 point-cloud messages (10 Hz; 200 per full episode)
`aeva_frames`	`IntField`	Number of Aeva Aeries II point-cloud messages (10 Hz; 194–200 per episode)
`blickfeld_frames`	`IntField`	Number of Blickfeld QB2 point-cloud messages (5 Hz; 95–101 per episode)
`topics`	`ListField(StringField)`	MCAP topic names present in the episode
`schemas`	`ListField(StringField)`	MCAP schema names used (`foxglove.PointCloud`, `foxglove.FrameTransform`)
`has_pointcloud`	`BooleanField`	`True` for all 30 episodes
`has_image`	`BooleanField`	`False` — no camera data in this dataset
`has_gps`	`BooleanField`	`False` — no GPS data
`has_imu`	`BooleanField`	`False` — no IMU data
`has_logs`	`BooleanField`	`False` — no log messages

MCAP Topic Structure (per episode)

Topic	Schema	Rate	Per-point fields
`/ouster/points`	`foxglove.PointCloud`	10 Hz	`x`, `y`, `z` (float32); `intensity`, `ambient`, `reflectivity` (float32); `channel_id` (float32)
`/aeva/points`	`foxglove.PointCloud`	10 Hz	`x`, `y`, `z` (float32); `intensity`, `ambient`, `reflectivity`, `velocity` (float32, radial m/s)
`/blickfeld/points`	`foxglove.PointCloud`	5 Hz	`x`, `y`, `z` (float32); `reflectivity`, `channel_id`, `horizontal_id` (float32)
`/tf_static`	`foxglove.FrameTransform`	static	Identity transforms for `ouster`, `aeva`, `blickfeld` frames relative to `world`

Typical point counts per frame: Ouster ~45K, Aeva ~92K, Blickfeld ~12K. Blickfeld has no intensity or ambient channel. Aeva's velocity field (radial velocity in m/s) is the unique FMCW capability enabling velocity-based dynamic-point labeling.

Ground Truth

No per-frame label files are stored in the FiftyOne dataset. The benchmark computes static/dynamic labels at evaluation time from parameters in metadata/ground_truth.yaml (in the original repository):

Ouster and Blickfeld: lane-aligned 3D bounding-box ROIs define dynamic regions; any point inside is labeled dynamic.
Aeva: a point is labeled dynamic if its measured radial velocity exceeds 1 m/s.

Parsing Decisions

Original records are protobuf .pb files decoded with highwayscene-proto==1.0.0. Each .pb → one .mcap using foxglove-sdk==0.26.0.
All uint16 auxiliary channels (intensity, ambient, reflectivity, channel_id, horizontal_id) are cast to float32 in the MCAP point-cloud buffers.
Points with range ≤ 0.5 m are filtered (zero-return guard).
Static transforms use foxglove.FrameTransform with no embedded timestamp, placing them in the viewer's static store for the full episode duration.
No extrinsic sensor-to-sensor calibration is provided by the dataset authors; all world → sensor transforms are identity.
Blickfeld operates at 5 Hz: frames where the sensor is absent are simply omitted from the /blickfeld/points topic.

Dataset Creation

Curation Rationale

Existing roadside LiDAR datasets focus on object detection or semantic segmentation with class-based labels, and are typically limited to a single sensor or scene. HighwayScene was created to fill the gap in systematic cross-sensor evaluation for static background subtraction — providing simultaneous recordings from three heterogeneous LiDAR technologies under real highway traffic conditions, with reproducible annotation strategies that do not require manual point-wise labeling.

Source Data

Data Collection and Processing

Data was recorded at a German highway construction site with a legally enforced 40 km/h speed limit. Sensors were mounted statically on roadside infrastructure. Recording rate is 10 Hz for the Ouster and Aeva sensors and 5 Hz for the Blickfeld. All streams are temporally synchronized. 5,998 global frames are captured across the full recording session (Feb 9, 2026), partitioned into train (frames 1–4000), val (4001–5000), and test (5001–5998) splits using a strict non-overlapping protocol.

Who are the source data producers?

Institute for Intelligent Systems (IIS), Esslingen University of Applied Sciences, Germany. Contact: alexander.baumann@hs-esslingen.de.

Annotations

Annotation process

Static/dynamic ground-truth labels are not stored per frame in the dataset records. Instead, two reproducible annotation strategies are defined in metadata/ground_truth.yaml:

Geometry-based (Ouster, Blickfeld): For each traffic lane, a static 3D bounding-box ROI aligned with the roadway is manually defined. Under empty-scene conditions no LiDAR returns fall inside these volumes. Since vehicles are constrained by concrete barriers, any return inside a lane volume originates from dynamic traffic. Points inside are labeled dynamic; all others static.
Velocity-based (Aeva): A point is labeled dynamic if its measured FMCW radial velocity exceeds 1 m/s. This strategy requires no scene geometry and illustrates the FMCW sensor's self-annotating capability.

Who are the annotators?

Annotation parameters (lane ROIs, ground planes) were defined by the dataset authors. No manual point-wise labeling was performed — labels are reproducibly derived from geometry and physics.

Personal and Sensitive Information

LiDAR point clouds do not record visually identifiable information (faces, license plates). No personal data is present in this dataset.

Citation

BibTeX:

@inproceedings{baumann2026beamwise,
  author    = {Alexander Baumann and Marcel Vo{\ss}hans and Thao Dang},
  title     = {Beam-Wise Statistical Background Subtraction for Static
               Roadside {LiDAR}: A Cross-Sensor Benchmark Study},
  booktitle = {IEEE International Conference on Intelligent Transportation
               Systems (ITSC)},
  year      = {2026}
}

APA:

Baumann, A., Voßhans, M., & Dang, T. (2026). Beam-Wise Statistical Background Subtraction for Static Roadside LiDAR: A Cross-Sensor Benchmark Study. IEEE International Conference on Intelligent Transportation Systems (ITSC).

More Information

Original dataset (protobuf format): https://huggingface.co/datasets/iis-esslingen/HighwayScene
Background subtraction code: https://github.com/HighwayScene/roadside-lidar-background-subtraction
Protobuf reader: https://github.com/HighwayScene/highwayscene-proto
Project page: https://highwayscene.github.io/

Dataset Card Authors

Harpreet Sahota

Dataset Card Contact

harpreetsahota07@gmail.com