The majority of human deaths and injuries are caused by traffic accidents. A million people worldwide die each year due to traffic accident injuries, consistent with the World Health Organization. Drivers who do not receive enough sleep, rest, or who feel weary may fall asleep behind the wheel, endangering both themselves and other road users. The research on road accidents specified that major road accidents occur due to drowsiness while driving. These days, it is observed that tired driving is the main reason to occur drowsiness. Now, drowsiness becomes the main principle for to increase in the number of road accidents. This becomes a major issue in a world which is very important to resolve as soon as possible. The predominant goal of all devices is to improve the performance to detect drowsiness in real time. Many devices were developed to detect drowsiness, which depend on different artificial intelligence algorithms. So, our research is also related to driver drowsiness detection which can identify the drowsiness of a driver by identifying the face and then followed by eye tracking. The extracted eye image is matched with the dataset by the system. With the help of the dataset, the system detected that if eyes were close for a certain range, it could ring an alarm to alert the driver and if the eyes were open after the alert, then it could continue tracking. If the eyes were open then the score that we set decreased and if the eyes were closed then the score increased. This paper focus to resolve the problem of drowsiness detection with an accuracy of 80% and helps to reduce road accidents.
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）
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）
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）
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）
DMD: A Large-Scale Multi-Modal Driver Monitoring Dataset for Attention and Alertness Analysis
Vicomtech Foundation, Basque Research and Technology Alliance (BRTA)（维科姆技术基金会，巴斯克研究与技术联盟）；ETS Ingenieros de Telecomunicación, Universidad Politécnica de Madrid（马德里理工大学电信工程学院）；Dependability Research Lab, Intel Labs Europe, Intel Deutschland GmbH（英特尔德国有限公司欧洲英特尔实验室可靠性研究实验室）
Hybrid Approach for Driver Behavior Analysis with Machine Learning, Feature Optimization, and Explainable AI
Department of CSE, DUET Gazipur, Bangladesh；Department of IST, George Mason University Fairfax, USA；AI \& Big Data Department, Woosong University Daejeon, South Korea
SAFE-D: A Spatiotemporal Detection Framework for Abnormal Driving Among Parkinson's Disease-like Drivers
Hong Kong JC STEM Lab of Smart City and Department of Computer Science, City University of Hong Kong（香港JC STEM实验室及城市大学计算机科学系）；Department of Computer Science and Engineering, Hong Kong University of Science and Technology（香港理工大学计算机科学与工程系）；Department of Electrical and Electronic Engineering, The University of Hong Kong（香港大学电子与电气工程系）
A Machine Learning Smartphone-based Sensing for Driver Behavior Classification
Higher School of Communications of Tunis, University of Carthage（迦太基大学突尼斯高等通信学院）；CEMSE Division, King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学（KAUST）计算工程与科学学院（CEMSE Division））
A Rapid Pattern-Recognition Method for Driving Types Using Clustering-Based Support Vector Machines
Beijing Institute of Technology（北京理工大学）；UC Berkeley（加州大学伯克利分校）
提出了一种快速模式识别方法来表征驾驶员曲线通过行为。为了缩短识别时间，提高驾驶风格的识别率，提出了一种基于k均值聚类的支持向量机（kMC-SVM）方法，并将其用于驾驶员的驾驶风格分类。首先将车速和节气门开度作为反映驾驶风格的特征参数。其次，为了区分驾驶员曲线通过行为，减少支持向量的数量，采用k-均值聚类方法对两类驾驶数据进行提取和聚类，缩短识别时间。然后，基于聚类结果，利用支持向量机方法生成超平面，用于判断和预测人类驾驶员属于哪种类型。最后，为了验证kMC-SVM方法的有效性，设计并进行了交叉验证实验。研究结果表明，与SVM方法相比，$ k $ MC-SVM方法是一种有效的驾驶风格分类方法，分类时间短。
ShrinkBox: Backdoor Attack on Object Detection to Disrupt Collision Avoidance in Machine Learning-based Advanced Driver Assistance Systems
eBRAIN Lab, New York University Abu Dhabi (NYUAD), UAE（eBRAIN实验室，纽约大学阿布扎赫尔分校（NYUAD），阿联酋）；AI and Digital Science Research Center, Technology Innovation Institute (TII), Abu Dhabi, UAE（人工智能与数字科学研究中心，技术创新研究所（TII），阿布扎赫尔，阿联酋）
Physics Guided Machine Learning Methods for Hydrology
Department of Computer Science and Engineering, University of Minnesota（计算机科学与工程系，明尼苏达大学）；Department of Bioproducts and Biosystems Engineering, University of Minnesota（生物产品与生物系统工程系，明尼苏达大学）；Department of Civil and Environmental Engineering, Pennsylvania State University（土木与环境工程系，宾夕法尼亚州立大学）；Department of Computer Science, University of Pittsburgh（计算机科学系，匹兹堡大学）
Forecasting precipitation in the Arctic using probabilistic machine learning informed by causal climate drivers
Department of Science and Engineering, Sorbonne University, Abu Dhabi, United Arab Emirates（Sorbonne大学科学与工程系）；Physics and Applied Mathematics Unit, Indian Statistical Institute, Kolkata 700108, India（印度统计研究院应用数学与物理单元）；Sorbonne Center for Artificial Intelligence, Sorbonne University, Paris, 75005, France（Sorbonne大学人工智能中心）；Indian Institute of Technology Gandhinagar, Gandhinagar 382355, India（印度理工学院冈丁加尔分校）；National Centre for Polar and Ocean Research, Ministry of Earth Sciences, Vasco da Gama, 403804, India（国家极地与海洋研究中心）；Center for Computational Natural Science and Bioinformatics, International Institute of Information Technology, Gachibowli, Hyderabad-500032, India（国际信息技术研究所计算自然科学与生物信息中心）；Department of Dynamics, Lodz University of Technology, Stefanowskiego 1/15, 90–537 Lodz, Poland（Lodz技术大学动力学系）；Centre for Mathematical Biology and Ecology, Department of Mathematics, Jadavpur University, Kolkata 700032, India（贾瓦德普大学数学生物学与生态中心）
Drivers' Manoeuvre Modelling and Prediction for Safe HRI
University of Manchester（曼彻斯特大学）；University of Bristol（布里斯托大学）
随着机器人和车辆等自主机器开始执行涉及人类用户的任务，确保它们之间的安全交互成为一个重要问题。将人机交互（HRI）研究的方法转化为人类与其他高度复杂的机器（例如半自动驾驶汽车）之间的交互，可以帮助推进这些机器在需要人类交互的场景中的使用。一种方法涉及理解人类的意图和决策，以估计人类在与机器人交互时的当前和不久的将来的动作。这一想法源于心理学概念“心理理论”（Theory of Mind），该概念已被广泛应用于机器人技术，最近还被应用于自动驾驶和半自动驾驶汽车。在这项工作中，我们探索了如何通过结合来自人体运动，车辆状态和人类输入（例如方向盘，踏板）的数据来预测人类的意图。基于递归神经网络模型的数据驱动的方法被用来对当前的驾驶操作进行分类，并预测未来要执行的操作。一个状态转换模型被用来与一组固定的机动标记数据记录在实时应用的试验。使用不同座椅偏好、驾驶专业知识和臂长的驾驶员对模型进行了训练和测试;机动识别的准确率和召回率指标超过95%，机动预测的准确率和召回率指标超过86%，已知和未知测试对象的预测时间窗口长达1秒。与我们以前的结果相比，性能提高和机动预测是可能的未知的测试科目，而不知道当前的机动。
