Automatic Clustering for Unsupervised Risk Diagnosis of Vehicle Driving for Smart Road
Institute for Infocomm Research, Agency for Science Technology and Research (A*STAR)（新加坡科技研究局信息通信研究院）；School of Microelectronics, Shanghai University（上海大学微电子学院）；School of Civil and Environmental Engineering, Nanyang Technological University（南洋理工大学土木与环境工程学院）；College of Transportation Engineering, Tongji University（同济大学交通运输工程学院）；Nanyang Business School, Nanyang Technological University（南洋理工大学南洋商学院）
Early risk diagnosis and driving anomaly detection from vehicle stream are of great benefits in a range of advanced solutions towards Smart Road and crash prevention, although there are intrinsic challenges, especially lack of ground truth, definition of multiple risk exposures. This study proposes a domain-specific automatic clustering (termed Autocluster) to self-learn the optimal models for unsupervised risk assessment, which integrates key steps of risk clustering into an auto-optimisable pipeline, including feature and algorithm selection, hyperparameter auto-tuning. Firstly, based on surrogate conflict measures, indicator-guided feature extraction is conducted to construct temporal-spatial and kinematical risk features. Then we develop an elimination-based model reliance importance (EMRI) method to unsupervised-select the useful features. Secondly, we propose balanced Silhouette Index (bSI) to evaluate the internal quality of imbalanced clustering. A loss function is designed that considers the clustering performance in terms of internal quality, inter-cluster variation, and model stability. Thirdly, based on Bayesian optimisation, the algorithm selection and hyperparameter auto-tuning are self-learned to generate the best clustering partitions. Various algorithms are comprehensively investigated. Herein, NGSIM vehicle trajectory data is used for test-bedding. Findings show that Autocluster is reliable and promising to diagnose multiple distinct risk exposures inherent to generalised driving behaviour. Besides, we also delve into risk clustering, such as, algorithms heterogeneity, Silhouette analysis, hierarchical clustering flows, etc. Meanwhile, the Autocluster is also a method for unsupervised multi-risk data labelling and indicator threshold calibration. Furthermore, Autocluster is useful to tackle the challenges in imbalanced clustering without ground truth or priori knowledge
National Taiwan University（台湾大学）；Carnegie Mellon University（卡内基梅隆大学）
In this paper, we propose a game theoretical adversarial intervention detection mechanism for reliable smart road signs. A future trend in intelligent transportation systems is ``smart road signs" that incorporate smart codes (e.g., visible at infrared) on their surface to provide more detailed information to smart vehicles. Such smart codes make road sign classification problem aligned with communication settings more than conventional classification. This enables us to integrate well-established results in communication theory, e.g., error-correction methods, into road sign classification pr…
Easydiagnos: a framework for accurate feature selection for automatic diagnosis in smart healthcare
Dr. B. C. Roy Engineering College（B.C.罗伊工程学院）；Bengal College of Engg. and Tech.（孟加拉工程技术学院）；National Institute of Technology（印度国家理工学院）；University of North Texas（北得克萨斯大学）
本文提出 Easydiagnos 框架，通过融合遗传算法、可解释 AI 和排列组合技术的 AFE 算法优化医疗特征选择，并在多个数据集上验证其可提升临床决策的准确性与可解释性。
Road crashes are the sixth leading cause of lost disability-adjusted life-years (DALYs) worldwide. One major challenge in traffic safety research is the sparsity of crashes, which makes it difficult to achieve a fine-grain understanding of crash causations and predict future crash risk in a timely manner. Hard-braking events have been widely used as a safety surrogate due to their relatively high prevalence and ease of detection with embedded vehicle sensors. As an alternative to using sensors fixed in vehicles, this paper presents a scalable approach for detecting hard-braking events using th…
Booz Allen Hamilton（博思艾伦汉密尔顿公司）；Virginia Tech（弗吉尼亚理工大学）
With the fast advancements of the Autonomous Vehicle (AV) industry, detection of Vulnerable Road Users (VRUs) using smartphones is critical for safety applications of Cooperative Intelligent Transportation Systems (C-ITSs). This study explores the use of low-power smartphone sensors and the Recurrence Quantification Analysis (RQA) features for this task. These features are computed over a thresholded similarity matrix extracted from nine channels: accelerometer, gyroscope, and rotation vector in each direction (x, y, and z). Given the high-power consumption of GPS, GPS data is excluded. RQA fe…
Universidad Autónoma de Chihuahua（奇瓦瓦自治大学）；Facultad de Ingeniería（工程学院）；Secretaría de Investigación y Posgrado（研究与研究生部）
Road transportation is of critical importance for a nation, having profound effects in the economy, the health and life style of its people. With the growth of cities and populations come bigger demands for mobility and safety, creating new problems and magnifying those of the past. New tools are needed to face the challenge, to keep roads in good conditions, their users safe, and minimize the impact on the environment. This dissertation is concerned with road quality assessment and aggressive driving, two important problems in road transportation, approached in the context of Intelligent Tran…
Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives
University of Applied Sciences（应用科学大学）；University of Skikda（斯基克达大学）；University of Dubai（迪拜大学）；University of Sharjah（沙迦大学）；De Montfort University（德蒙福特大学）；Qatar University（卡塔尔大学）；University of Klagenfurt（克拉根福大学）
Queen’s University（女王大学）；Royal Military College of Canada（加拿大皇家军事学院）；Qatar University（卡塔尔大学）
Owing to the expeditious growth in the information and communication technologies, smart cities have raised the expectations in terms of efficient functioning and management. One key aspect of residents' daily comfort is assured through affording reliable traffic management and route planning. Comprehensively, the majority of the present trip planning applications and service providers are enabling their trip planning recommendations relying on shortest paths and/or fastest routes. However, such suggestions may discount drivers' preferences with respect to safe and less disturbing trips. Road …
Shahrood University of Technology（沙赫鲁德理工大学）；Louisiana State University（路易斯安那州立大学）；Ton Duc Thang University（同塔大学）；Oxford Brookes University（牛津布鲁克斯大学）；Bauhaus University Weimar（魏玛包豪斯大学）；Queensland University of Technology（昆士兰科技大学）；Duy Tan University（岘港大学（越南东塔大学））；J. Selye University（塞莱大学）；Obuda University（奥布达大学）
Prediction models in mobility and transportation maintenance systems have been dramatically improved through using machine learning methods. This paper proposes novel machine learning models for intelligent road inspection. The traditional road inspection systems based on the pavement condition index (PCI) are often associated with the critical safety, energy and cost issues. Alternatively, the proposed models utilize surface deflection data from falling weight deflectometer (FWD) tests to predict the PCI. Machine learning methods are the single multi-layer perceptron (MLP) and radial basis fu…
iWatchRoad: Scalable Detection and Geospatial Visualization of Potholes for Smart Cities
National Institute of Science Education and Research (NISER), An OCC of Homi Bhabha National Institute（国家科学教育研究所（NISER），霍米·巴哈瓦国家研究所的下属机构）；Silicon University（硅大学）
Shanghai Jiao Tong University（上海交通大学）；University College London（伦敦大学学院）；Noah’s Ark Lab, Huawei Technologies（华为技术有限公司诺亚方舟实验室）
Multi-agent interaction is a fundamental aspect of autonomous driving in the real world. Despite more than a decade of research and development, the problem of how to competently interact with diverse road users in diverse scenarios remains largely unsolved. Learning methods have much to offer towards solving this problem. But they require a realistic multi-agent simulator that generates diverse and competent driving interactions. To meet this need, we develop a dedicated simulation platform called SMARTS (Scalable Multi-Agent RL Training School). SMARTS supports the training, accumulation, an…
Entropy-based machine learning model for diagnosis and monitoring of Parkinson's Disease in smart IoT environment
Institute of Physics and Technology, Petrozavodsk State University（彼得罗扎沃茨克国立大学物理与技术学院）；Kuwait College of Science and Technology（科威特科学技术学院）；Universiti Malaysia Perlis（马来西亚玻璃市大学）；Institute of Mathematics and Information Technology, Petrozavodsk State University（彼得罗扎沃茨克国立大学数学与信息技术学院）
Texas A&M University（德克萨斯A&M大学）；Texas A&M University at Galveston（德克萨斯A&M大学加尔维斯顿分校）；University of Delaware（特拉华大学）
The objective of this study is to predict road flooding risks based on topographic, hydrologic, and temporal precipitation features using machine learning models. Predictive flood monitoring of road network flooding status plays an essential role in community hazard mitigation, preparedness, and response activities. Existing studies related to the estimation of road inundations either lack observed road inundation data for model validations or focus mainly on road inundation exposure assessment based on flood maps. This study addresses this limitation by using crowdsourced and fine-grained tra…
Politecnico di Milano（米兰理工大学）；Kubris S.r.l.（Kubris有限公司）
Smartphone usage while driving is unanimously considered to be a really dangerous habit due to strong correlation with road accidents. In this paper, the problem of detecting whether the driver is using the phone during a trip is addressed. To do this, high-frequency data from the triaxial inertial measurement unit (IMU) integrated in almost all modern phone is processed without relying on external inputs so as to provide a self-contained approach. By resorting to a frequency-domain analysis, it is possible to extract from the raw signals the useful information needed to detect when the driver…
