Employing Sensors and Services Fusion to Detect and Assess Driving Events

Hosseinioun, Seyed Vahid; Al-Osman, Hussein; Saddik, Abdulmotaleb El

doi:10.1109/ism.2015.121

Cited by 11 publications

(4 citation statements)

References 6 publications

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“…However, 27.9% of them used external sensors or hardware connected to their smartphones in addition to the built-in sensors of mobile devices. Some studies have reported the use of external sensors in conjunction with embedded sensors in mobile devices to address traffic issues ( 8 , 12 , 13 , 15 , 17 , 18 , 22 , 24 , 29 , 36–41 ). Camera sensors, for example, collect data from various sources such as facial and eye expressions, road images, road potholes, and traffic signs ( 19 , 21 , 25 , 31 , 42–45 ).…”

Section: Resultsmentioning

confidence: 99%

Functionalities of Mobile Solutions for Preventing Road Traffic Health and Safety Issues in the Context of Information Management Cycle

Aghayari,

Kalankesh,

Sadeghi-Bazargani

et al. 2023

Transportation Research Record: Journal of the Transportation R

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In the present study, researchers thoroughly analyzed the peer-reviewed literature on mobile solutions for road traffic health and safety issues with regard to their functionalities in the context of information management. A comprehensive review of papers reporting mobile solutions for road traffic health and safety was conducted using relevant eligibility criteria. Electronic sources were searched using a combination of keywords. To analyze the mobile solutions reported in the studies, the researchers deployed different aspects of the information management cycle and functionality of apps for preventing road traffic health and safety issues. Overall, 68 studies met the inclusion criteria and were included in the final analysis. A total of 72.1% of the studies reported using only the in-built sensors of smartphones in their solutions for data collection. Several processing approaches for the data were found, which can be referred to as threshold value setting, image processing, fuzzy logic, and decision tree. According to the finding, the highest percentage of processing techniques utilized in the mobile solutions belonged to threshold value setting technique (23.5%) followed by image processing technique (10.3%). The applications of mobile solutions in studies commonly include recording and providing feedback on driving behavior, real-time alerts for road conditions, accident detection, and drowsiness management. Finally, fulfilling these targets could affect humans, the physical environment, and the vehicle factors toward preventing road traffic health and safety issues. The domain researchers and developers of mobile solutions can revisit and enhance the functionalities with attention to the full cycle of information management, including data collection, data analysis, interventions, and data application in their initiatives.

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Section: Resultsmentioning

confidence: 99%

Functionalities of Mobile Solutions for Preventing Road Traffic Health and Safety Issues in the Context of Information Management Cycle

Aghayari,

Kalankesh,

Sadeghi-Bazargani

et al. 2023

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

show abstract

“…e high cost and complexity of multi-sensor-based systems mean that it is not practical to deploy within a vehicle for daily users, and an e cient approach utilizing available sensors in smartphones or data recorders (including a GPS receiver or 3-axis accelerometer) is required. Unlike some costly methods, smartphones with some applications installed could also provide reliable information for identifying some typical risky driving behaviors, such as the percentage in excess of the legal speed limit, or the frequency of abrupt acceleration and hard braking [13]. Researchers Johnson and Trivedi [14] were able to determine driving style with smartphones by using the dynamic time warping algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

A Case Study in China to Determine Whether GPS Data and Derivative Indicator Can Be Used to Identify Risky Drivers

Tong

Yu-xi

et al. 2019

Journal of Advanced Transportation

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This paper presents an investigation of the relationship between driver risk and factors indicating vehicle’s speed and driver’s acceleration behavior. The main objective is to examine whether GPS data and derivative indicator can be used to identify risky drivers by means of factor analysis. In doing so, a real road driving experiment is conducted to collect data. Fifty drivers are asked to drive along a route which includes both rural highways and urban roads. The trajectories are recorded by GPS devices to calculate speed and derive acceleration measures. Driver’s behavior is also recorded by cameras and analyzed by another group of volunteers to determine whether the driver is risky or not. The drivers are then classified into five groups with different levels of risk based on the scores obtained through factor analysis. The results are verified by the volunteer's categorization and further evaluated by symbolic aggregate approximation. A binary logistic regression model is established ultimately for predicting high-risk drivers. The potential applications of this study include developing quantitative measures to identify risky drivers, especially for auto-insurance companies with usage-based insurance (UBI) applications, bus companies, and transport enterprises.

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“…Thresholding. [257] x x x x Various machine learning methods. [258] x x x x x x x Various machine learning methods.…”

Section: Driver Behavior Classificationmentioning

confidence: 99%

Smartphone-Based Vehicle Telematics: A Ten-Year Anniversary

Wahlström

Skog

Händel

2017

IEEE Trans. Intell. Transport. Syst.

157

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Abstract-Just like it has irrevocably reshaped social life, the fast growth of smartphone ownership is now beginning to revolutionize the driving experience and change how we think about automotive insurance, vehicle safety systems, and traffic research. This paper summarizes the first ten years of research in smartphone-based vehicle telematics, with a focus on userfriendly implementations and the challenges that arise due to the mobility of the smartphone. Notable academic and industrial projects are reviewed, and system aspects related to sensors, energy consumption, cloud computing, vehicular ad hoc networks, and human-machine interfaces are examined. Moreover, we highlight the differences between traditional and smartphonebased automotive navigation, and survey the state-of-the-art in smartphone-based transportation mode classification, driver classification, and road condition monitoring. Future advances are expected to be driven by improvements in sensor technology, evidence of the societal benefits of current implementations, and the establishment of industry standards for sensor fusion and driver assessment.

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Employing Sensors and Services Fusion to Detect and Assess Driving Events

Cited by 11 publications

References 6 publications

Functionalities of Mobile Solutions for Preventing Road Traffic Health and Safety Issues in the Context of Information Management Cycle

Functionalities of Mobile Solutions for Preventing Road Traffic Health and Safety Issues in the Context of Information Management Cycle

A Case Study in China to Determine Whether GPS Data and Derivative Indicator Can Be Used to Identify Risky Drivers

Smartphone-Based Vehicle Telematics: A Ten-Year Anniversary

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