Singapore AutoMan@NTU
Distracted Driving (SAM-DD) Dataset
Haohan Yang, Haochen Liu, Zhongxu Hu, Chen Lv
Nanyang Technological University

Datasets

SAM-DD
Features
  • The SAM-DD dataset contains high-quality multi-modal information, i.e., RGB and depth, which can improve the model’s reliability against various driving environments.
  • The SAM-DD dataset is mainly for intelligent driving research in the laboratory, including driving takeover systems, remote driving, and control strategies involving driver states, etc.
  • SAM-DD dataset is large enough for training learning-based models from scratch. Also, researchers can conveniently migrate the trained model to targeted downstream tasks.
Illustration Coming soon! Coming soon!
Download link SAM-DD(RGB).rar SAM-DD(Pseudo-color).rar SAM-DD(Depth)
Citation
  • H. Yang, H. Liu, Z. Hu, A.T. Nguyen, T.M. Guerra, and C. Lv, "Quantitative Identification of Driver Distraction: A Weakly Supervised Contrastive Learning Approach," IEEE Trans. Intell. Transp. Syst., vol. 25, no. 2, pp. 2034-2045, Feb. 2024.

Selected Publications

A series of studies have been carried out based on SAM-DD, representative ones are listed below,

Institutions

Our work is being used by researchers across academia and research labs in 3 countries and 5 institutions:

NTU

UPHF

ISU

SU

NYU

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