IRO Journals
Journal of Trends in Computer Science and Smart Technology

GraDE: A Severity-Graded Dashcam Event Dataset with Per-Video Adaptive Segmentation

Open Access
Published: 27 October, 2026
Pages: 1018-1045
  • Mittal Darji , Mittal Darji
    Research Scholar, CVM University, Vallabh Vidyanagar, Gujarat, India
    Research Scholar, CVM University, Vallabh Vidyanagar, Gujarat, India
  • Nikhil Gondaliya Nikhil Gondaliya
    Professor and Head, Department of Information Technology, G H Patel College of Engineering & Technology, CVM University, Vallabh Vidyanagar, Gujarat, India
    Professor and Head, Department of Information Technology, G H Patel College of Engineering & Technology, CVM University, Vallabh Vidyanagar, Gujarat, India
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How to Cite

Darji, Mittal, and Nikhil Gondaliya. 2026. “GraDE: A Severity-Graded Dashcam Event Dataset With Per-Video Adaptive Segmentation”. Journal of Trends in Computer Science and Smart Technology 8 (4): 1018-45. https://doi.org/10.36548/jtcsst.2026.4.006.
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Keywords

GraDE Dataset
Dataset Construction
Dashcam Video
Near-Miss Detection
Crash Severity Grading

Abstract

The ego-view traffic datasets generally formulate crashes as a binary recognition problem, in which near-misses are largely excluded and collisions are not graded based on their physical severity. GraDE (Graded Dashcam Events) aims to fill this gap by providing a total of 1,321 videos collected from 25 public dashcam compilation uploads. Videos are categorized into three classes: Non-Crash, Near-Miss, and Crash. Among the 487 Crash videos, 433 have a Minor or Major grade indicating their severity as seen in the video footage. A second annotator rated a predefined set of 297 videos; Cohen’s kappa was 0.656 for the 243 videos that neither of the two annotators considered ambiguous. In the compilation videos, unrelated events are spliced together by an editor. To select the clips from compilation videos, we utilized QualityGridAuto, which searches fourteen candidate distance thresholds for each source video and evaluates their quality based on usable duration, boundary strength, within-segment similarity. It applies adaptive thresholding to the construction of the dataset rather than developing a new segmentation method. The frames were sampled at 1 fps and embedded at 224 × 224 pixels, while each baseline utilized sixteen frames with the same resolution. In the manual audit, 93.2%of inspected clips contained one continuous scene. With source-pure four-fold testing, the CNN + BiLSTM baseline reached macro-F1 0.8725 on the three event classes and Video Swin-T 0.9053, but CNN + BiLSTM fell to 0.6889 when the two severity grades were added. On dedicated Minor versus Major classification the two models reached 0.5987 and 0.6510, so severity grading remains substantially harder than event classification. GraDE is released as an annotation index.

References

  1. Park, Jong-Il, Sungyop Kim, and Joon-Ki Kim. “Exploring Spatial Associations between Near-Miss and Police-Reported Crashes: The Heinrich’s Law in Traffic Safety.” Transportation research interdisciplinary perspectives 19 (2023): 100830.
  2. Dingus, Thomas A., Sheila G. Klauer, Vicki Lewis Neale, Andy Petersen, Suzanne E. Lee, Jeremy Sudweeks, Miguel A. Perez et al. “The 100-Car Naturalistic Driving Study, Phase II-Results of the 100-Car Field Experiment.” (2006).
  3. Bao, Wentao, Qi Yu, and Yu Kong. “Uncertainty-based Traffic Accident Anticipation with Spatio-Temporal Relational Learning.” In Proceedings of the 28th ACM International Conference on Multimedia, 2020, 2682-2690.
  4. Chan, Fu-Hsiang, Yu-Ting Chen, Yu Xiang, and Min Sun. “Anticipating Accidents in Dashcam Videos.” In Asian conference on computer vision, Cham: Springer International Publishing, 2016, 136-153.
  5. Moura, Daniel, Shizhan Zhu, and Orly Zvitia. “Nexar Dashcam Collision Prediction Dataset and Challenge.” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025, 2608-2616.
  6. Xu, Chuan, Jingqin Gao, Fan Zuo, and Kaan Ozbay. “Estimating Urban Traffic Safety and Analyzing Spatial Patterns through the Integration of City-Wide Near-Miss Data: A New York City Case Study.” Applied Sciences 14, no. 14 (2024): 6378.
  7. Jaradat, Shadi, Mohammed Elhenawy, Huthaifa I. Ashqar, Alexander Paz, and Richi Nayak. “Leveraging Deep Learning and Multimodal Large Language Models for Near-Miss Detection Using Crowdsourced Videos.” IEEE Open Journal of the Computer Society 6 (2025): 223-235.
  8. Yao, Yu, Xizi Wang, Mingze Xu, Zelin Pu, Yuchen Wang, Ella Atkins, and David J. Crandall. “DoTA: Unsupervised Detection of Traffic Anomaly in Driving Videos.” IEEE transactions on pattern analysis and machine intelligence 45, no. 1 (2022): 444-459.
  9. Fang, Jianwu, Dingxin Yan, Jiahuan Qiao, Jianru Xue, He Wang, and Sen Li. “Dada-2000: Can Driving Accident Be Predicted by Driver Attentionƒ Analyzed by a Benchmark.” In 2019 IEEE Intelligent Transportation Systems Conference (ITSC), IEEE, 2019, 4303-4309.
  10. Fang, Jianwu, Lei-lei Li, Junfei Zhou, Junbin Xiao, Hongkai Yu, Chen Lv, Jianru Xue, and Tat-Seng Chua. “Abductive Ego-View Accident Video Understanding for Safe Driving Perception.” In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2024, 22030-22040.
  11. Rocky, Arash, Qingming Jonathan Wu, and Wandong Zhang. “Review of Accident Detection Methods Using Dashcam Videos for Autonomous Driving Vehicles.” IEEE Transactions on Intelligent Transportation Systems 25, no. 8 (2024): 8356-8374.
  12. Bertasius, G., H. Wang, and L. Torresani. “Is Space-Time Attention All You Need for Video Understanding?” In Proceedings of the International Conference on Machine Learning, 2021, 813–824.
  13. Tong, Zhan, Yibing Song, Jue Wang, and Limin Wang. “Videomae: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training.” Advances in neural information processing systems 35 (2022): 10078-10093.
  14. Tami, M. A., H. I. Ashqar, M. Elhenawy, et al. “Using Multimodal Large Language Models for Automated Detection of Traffic Safety-Critical Events.” Vehicles 6, no. 3 (2024): 1571–1590.
  15. Zhang, R., B. Wang, J. Zhang, Z. Bian, C. Feng, and K. Ozbay. “When Language and Vision Meet Road Safety: Leveraging Multimodal Large Language Models for Video-Based Traffic Accident Analysis.” Accident Analysis & Prevention 219 (2025): 108077.
  16. Desai, N. P., A. Etemad, and M. Greenspan. “CycleCrash: A Dataset of Bicycle Collision Videos for Collision Prediction and Analysis.” In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2025, 8460–8470.
  17. Li, W., H. Xie, and P. Lin. “DSGF-YOLO: A Lightweight Deep Neural Network for Traffic Accident Detection and Severity Classifications.” Vehicles 7, no. 4 (2025): 153.
  18. Thounaojam, D. M., K. Rathi, and S. Roy. “Video Shot-Boundary Detection: Issues, Challenges and Solutions.” Artificial Intelligence Review 57 (2024): 104.
  19. Souček, T., and J. Lokoč. “TransNet V2: An Effective Deep Network Architecture for Fast Shot Transition Detection.” In Proceedings of the ACM International Conference on Multimedia, 2024, 11218–11221.
  20. Santos, K., J. P. Dias, and C. Amado. “A Literature Review of Machine Learning Algorithms for Crash Injury Severity Prediction.” Journal of Safety Research 80 (2022): 254–269.
  21. Chen, J., P. Liu, S. Wang, N. Zheng, and X. Guo. “Prediction and Interpretation of Crash Severity Using Machine Learning based on Imbalanced Traffic Crash Data.” Journal of Safety Research 93 (2025): 185–199.
  22. Deng, J., W. Dong, R. Socher, et al. “ImageNet: A Large-Scale Hierarchical Image Database.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2009, 248–255.
  23. He, K., X. Zhang, S. Ren, et al. “Deep Residual Learning for Image Recognition.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, 770–778.
  24. Gebru, T., J. Morgenstern, B. Vecchione, et al. “Datasheets for Datasets.” Communications of the ACM 64, no. 12 (2021): 86–92.
  25. Wilkinson, M. D., M. Dumontier, I. J. Aalbersberg, et al. “The FAIR Guiding Principles for Scientific Data Management and Stewardship.” Scientific Data 3 (2016): 160018.
  26. Hochreiter, S., and J. Schmidhuber. “Long Short-Term Memory.” Neural Computation 9, no. 8 (1997): 1735–1780.
  27. Liu, Z., J. Ning, Y. Cao, Y. Wei, Z. Zhang, S. Lin, and H. Hu. “Video Swin Transformer.” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, 3202–3211.
  28. Cohen, J. “A Coefficient of Agreement for Nominal Scales.” Educational and Psychological Measurement 20, no. 1 (1960): 37–46.
  29. Geirhos, R., J.-H. Jacobsen, C. Michaelis, et al. “Shortcut Learning in Deep Neural Networks.” Nature Machine Intelligence 2, no. 11 (2020): 665–673.