SafeCritCR

This dataset consists of 1,200 videos of CommonRoad scenarios that include 8 different types of safety criticalities. They have been rendered using the Frenetix Motion Planner.

The complete set of samples follows a longtail distribution in terms of their labels. We constructed a uniformly distributed subset with 5 classes and 300 entries. Videos in this subset are tagged with "in_balanced_subset": true.

Videos

The MP4s depict the scenarios from a bird's eye view following the centered ego vehicle at 2fps. The ego vehicle is blue, while the other vehicles are drawn in a complementaty orange, and with a unique ID rendered above them. Lane trajectories that are currently blocked by traffic lights are highlighted using red lines.

Labels

The scenarios were labled using the following criteria. If several labels apply, the safety criticality with the most immediate threat to the ego vehicle was chosen.

Safety Criticality	Criteria
No danger	The ego vehicle is safe or none of the other labels apply.
Being rear-ended	Another vehicle approaches from the rear and collides, or is about to collide with the ego vehicle, particularly when the ego vehicle is stationary or braking.
Running red light	The ego vehicle fails to stop at a red light.
Rear-ending leading vehicle	The ego vehicle collides, or is about to collide with a leading vehicle.
Not following lane	The ego vehicle is not correctly following its lane, e.g., swerving, driving into the oncoming lane, reversing without reason, or stopping without reason. Properly changing lanes does not count.
Crossing active lane	The ego vehicle enters or crosses a lane currently occupied by oncoming or cross-traffic, failing to yield to the active flow and creating an immediate collision risk.
Merging into active lane	The ego vehicle merges into an active lane without properly yielding and thereby causing a collision or near collision.
Other vehicle not yielding	Another vehicle fails to concede the right of way to the ego vehicle in a merge, intersection, or lane change scenario, proceeding aggressively and causing or risking a collision.

Acknowledgments & Generation Pipeline

The videos of this dataset were generated using a combination of open-source autonomous driving frameworks:

• Scenarios: The base .xml scenarios were sourced from CommonRoad.
• Rendering & Motion: The scenarios were rendered into bird's-eye-view videos using the Frenetix Motion Planner.

If you use this dataset, we encourage you to also explore and cite the underlying frameworks that made it possible:

• CommonRoad: Althoff, M., et al. (2017). "CommonRoad: Composable benchmarks for motion planning on roads." IEEE Intelligent Vehicles Symposium (IV).
• FRENETIX: Trauth, R., et al. (2024). "FRENETIX: A High-Performance and Modular Motion Planning Framework for Autonomous Driving."

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Third-Party Data License (CommonRoad XML Source - BSD 3-Clause): Copyright 2019 Technical University of Munich, Professorship of Cyber-Physical Systems.

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