Title | Naturalistic driving data for a smart cloud-based abnormal driving detector |
Creator | |
Date Issued | 2018-06-26 |
Conference Name | IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation Conference (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) |
Source Publication | 2017 IEEE SmartWorld Ubiquitous Intelligence and Computing, Advanced and Trusted Computed, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation, SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2017 - Conference Proceedings
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ISBN | 978-1-5386-0435-9 |
Pages | 1-8 |
Conference Date | AUG 04-08, 2017 |
Conference Place | San Francisco, U.S.A. |
Abstract | This paper examines a possible implementation of video and radar data into smart abnormal driving behavior detecting systems. A prevailing research is SafeDrive, which positions the application in a successful trajectory in identifying driving anomalies to improve societal transportation safety. However, there are limitations of road and environment data that could allow mobile phone-based applications to accurately evaluate driving abnormality. In this paper we propose utilizing external research from the Second Strategic Highway Research Program (SHRP2), which can provide video and radar data for the improvement of detecting and evaluating a driver's atypical behaviors. The collection of data allows a deep study on safety, renewal, capacity, and reliability in a vehicle's actions that could permit transportation organizations to efficiently assess security operatives. Additionally, the evaluation of gathered data encompasses tools that determine and discuss features of both a vehicle and its driver, which influence driving styles linked to the near-crash or crash events. The Naturalistic Driving Data (NDD), supplies detailed information and examination of a human-vehicle-interaction to better comprehend abnormal driving behavior and critical factors that lead to most traffic accidents. |
Keyword | Big-Data Driving Behavior Human-Vehicle Interaction Naturalistic Driving Studies Near Crash SHRP2 |
DOI | 10.1109/UIC-ATC.2017.8397449 |
URL | View source |
Indexed By | CPCI-S |
Language | 英语English |
WOS Research Area | Computer Science ; Engineering |
WOS Subject | Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
WOS ID | WOS:000464418300058 |
Scopus ID | 2-s2.0-85050205183 |
Citation statistics | |
Document Type | Conference paper |
Identifier | http://repository.uic.edu.cn/handle/39GCC9TT/7180 |
Collection | Research outside affiliated institution |
Affiliation | 1.Department of Computer and Information Sciences, Fordham University, 10458, United States 2.School of Computer Science and Educational Software, Guangzhou University, Guangzhou, 510006, China 3.College of Computer Science and Technology, Huaqiao University, Xiamen, 361021, China 4.College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia |
Recommended Citation GB/T 7714 | Rosales, Athina,Alam Bhuiyan, M. Z.,Wang, Guojunet al. Naturalistic driving data for a smart cloud-based abnormal driving detector[C], 2018: 1-8. |
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