mlsanityData to 7 Oct 2026

CycleCrash: A Dataset of Bicycle Collision Videos for Collision Prediction and Analysis

arXiv 2409.19942 ↗First posted 30 Sep 2024cs.CVcited by 3

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Abstract (arXiv)

Self-driving research often underrepresents cyclist collisions and safety. To address this, we present CycleCrash, a novel dataset consisting of 3,000 dashcam videos with 436,347 frames that capture cyclists in a range of critical situations, from collisions to safe interactions. This dataset enables 9 different cyclist collision prediction and classification tasks focusing on potentially hazardous conditions for cyclists and is annotated with collision-related, cyclist-related, and scene-related labels. Next, we propose VidNeXt, a novel method that leverages a ConvNeXt spatial encoder and a non-stationary transformer to capture the temporal dynamics of videos for the tasks defined in our dataset. To demonstrate the effectiveness of our method and create additional baselines on CycleCrash, we apply and compare 7 models along with a detailed ablation. We release the dataset and code at https://github.com/DeSinister/CycleCrash/ .

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 37 of its 68 references that are in the map. First posted on arXiv 30 Sep 2024.

Builds on

37 of 68 references.

References · 37

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25 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Simplifying Traffic Anomaly Detection with Video Foundation Modelscited by 3
  2. 2025SEPose: A Synthetic Event-based Human Pose Estimation Dataset for Pedestrian Monitoringcited by 2
  3. 2026CollideNet: Hierarchical Multi-scale Video Representation Learning with Disentanglement for Time-To-Collision Forecastingcited by 0