This paper addresses the learning task of estimating driver drowsiness from the signals of car acceleration sensors. Since even drivers themselves cannot perceive their own drowsiness in a timely manner unless they use burdensome invasive sensors, obtaining labeled training data for each timestamp is not a realistic goal. To deal with this difficulty, we formulate the task as a weakly supervised learning. We only need to add labels for each complete trip, not for every timestamp independently. By assuming that some aspects of driver drowsiness increase over time due to tiredness, we formulate an algorithm that can learn from such weakly labeled data. We derive a scalable stochastic optimization method as a way of implementing the algorithm. Numerical experiments on real driving datasets demonstrate the advantages of our algorithm against baseline methods.
An EEG Channel Selection Framework for Driver Drowsiness Detection via Interpretability Guidance
Nanyang Technological University（南洋理工大学）；Continental-NTU Corporate Lab（大陆集团-南洋理工大学联合实验室）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）
LDGCN: An Edge-End Lightweight Dual GCN Based on Single-Channel EEG for Driver Drowsiness Monitoring
College of Computer and Big Data, Fuzhou University（福州大学计算机与大数据学院）；Polytechnic University of Catalonia（加泰罗尼亚理工大学）；New Engineering Industry College, Putian University（莆田大学新工程产业学院）；School of Computer and Artificial Intelligence, Zhengzhou University（郑州大学计算机与人工智能学院）；College of Computer and Control Engineering, Minjiang University（闽江学院计算机与控制工程学院）；Sichuan Conservatory of Music（四川音乐学院）；Fuzhou Technology Innovation Center of Intelligent Manufacturing information System, Minjiang University（闽江学院福州智能制造信息系统技术创新中心）；Fujian Polytechnic Normal University（福建技术师范学院）
Offline EEG-Based Driver Drowsiness Estimation Using Enhanced Batch-Mode Active Learning (EBMAL) for Regression
DataNova；U.S. Army Research Laboratory（美国陆军研究实验室）；University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；DCS Corp（DCS公司）；National Chiao-Tung University（国立交通大学）；University of Technology, Sydney（悉尼科技大学）
Blinking Beyond EAR: A Stable Eyelid Angle Metric for Driver Drowsiness Detection and Data Augmentation
Institue of Smart Engineering and Machine Elements at the Hamburg University of Technology（汉堡技术大学智能工程与机械元件研究所）；Intelligent Transport Systems group at the Australian Center for Robotics, The University of Sydney（澳大利亚机器人中心智能交通系统组）
Adversarial Robustness and Feature Impact Analysis for Driver Drowsiness Detection
Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD), School of Engineering, Polytechnic of Porto (ISEP/IPP)（波尔图理工学院工程学院智能工程与先进创新发展计算研究组（GECAD））；CardioID Technologies（CardioID科技公司）
Driver Drowsiness Estimation from EEG Signals Using Online Weighted Adaptation Regularization for Regression (OwARR)
DataNova；U.S. Army Research Laboratory（美国陆军研究实验室）；University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；DCS Corp（DCS公司）；University of Technology, Sydney（悉尼科技大学）；National Chiao-Tung University（国立交通大学）
An Intelligent Safety System for Human-Centered Semi-Autonomous Vehicles
Institute for Advanced Studies in Basic Sciences (IASBS)（基础科学高级研究学院）；Charles University（查理大学）；Institute for Research in Fundamental Sciences (IPM)（基础科学研究所）
A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics
Institute of Systems Engineering, College of Management and Economics, Tianjin University（系统工程研究所，管理经济学院，天津大学）；College of Automation and Electronic Engineering, Qingdao University of Science and Technology（自动化与电子工程学院，青岛科技大学）；College of Automation, Chongqing University of Posts and Telecommunications（自动化学院，重庆邮电大学）
Markov Regime-Switching Intelligent Driver Model for Interpretable Car-Following Behavior
Department of Civil Engineering, McGill University, Montreal, QC H3A 0C3, Canada；Laboratory for Information \& Decision Systems (LIDS), Massachusetts Institute of Technology, Cambridge, MA 02139, USA
