Smartphone-Based Vehicle Telematics: A Ten-Year Anniversary

Wahlström, Johan; Skog, Isaac; Händel, Peter

doi:10.1109/tits.2017.2680468

Cited by 150 publications

(80 citation statements)

References 237 publications

(301 reference statements)

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“…Traditional vision-based or laser-based road anomaly detection techniques are time-consuming or labor intensive. 5 With the development of mobile devices and sensor technologies, it is possible to detect roadway anomalies using crowdsourcing-based sensor data. An anomaly is any permanent obstacle generated by the continuous use, weather conditions, or traffic planning decisions in the road.…”

Section: Introductionmentioning

confidence: 99%

Enabling real-time road anomaly detection via mobile edge computing

Zheng

Zhou

Chen

et al. 2019

International Journal of Distributed Sensor Networks

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To discover road anomalies, a large number of detection methods have been proposed. Most of them apply classification techniques by extracting time and frequency features from the acceleration data. Existing methods are time-consuming since these methods perform on the whole datasets. In addition, few of them pay attention to the similarity of the data itself when vehicle passes over the road anomalies. In this article, we propose QF-COTE, a real-time road anomaly detection system via mobile edge computing. Specifically, QF-COTE consists of two phases: (1) Quick filter. This phase is designed to roughly extract road anomaly segments by applying random forest filter and can be performed on the edge node.(2) Road anomaly detection. In this phase, we utilize collective of transformation-based ensembles to detect road anomalies and can be performed on the cloud node. We show that our method performs clearly beyond some existing methods in both detection performance and running time. To support this conclusion, experiments are conducted based on two real-world data sets and the results are statistically analyzed. We also conduct two experiments to explore the influence of velocity and sample rate. We expect to lay the first step to some new thoughts to the field of real-time road anomalies detection in subsequent work.

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Section: Introductionmentioning

confidence: 99%

Enabling real-time road anomaly detection via mobile edge computing

Zheng

Zhou

Chen

et al. 2019

International Journal of Distributed Sensor Networks

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“…A survey conducted by Wahlstrom et al in 2014 shows that more than 13 billion wireless fidelity (Wi-Fi) devices will be activated by 2020 [1]. Modern mobile devices are equipped with multiple wireless technologies, such as Bluetooth, Wi-Fi, and cellular radio.…”

Section: Introductionmentioning

confidence: 99%

Socially-aware congestion control in ad-hoc networks: Current status and the way forward

Liaqat

Ali

Qadir

et al. 2019

Future Generation Computer Systems

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Ad-hoc social networks (ASNETs) represent a special type of traditional ad-hoc network in which a user's social properties (such as the social connections and communications metadata as well as application data) are leveraged for offering enhanced services in a distributed infrastructureless environments. However, the wireless medium, due to limited bandwidth, can easily suffer from the problem of congestion when social metadata and application data are exchanged among nodes-a problem that is compounded by the fact that some nodes may act selfishly and not share its resources. While a number of congestion control schemes have been proposed for the traditional ad-hoc networks, there has been limited focus on incorporating social awareness into congestion control schemes. We revisit the existing traditional ad-hoc congestion control and data distribution protocols and motivate the need for embedding social awareness into these protocols to improve performance. We report that although some work is available in opportunistic network that uses sociallyaware techniques to control the congestion issue, this area is largely unexplored and warrants more research attention. In this regards, we highlight the current research progress and identify multiple future directions of research.

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“…The primary objective of these studies are to derive an estimate of risk involved in the very nature of driving a land vehicle. Wahlström et al [2] provide us with a very detailed review of the work done (smartphone based telematics) in the last tenyear span. A large body of the works referred by Wahlström et al relates to the development of industrial applications like UBI (Usage Based Insurance) and connected car scenarios.…”

Section: Introductionmentioning

confidence: 99%

Investigations on Driver Unique Identification from Smartphone’s GPS Data Alone

Chowdhury

Chakravarty

Ghose

et al. 2018

Journal of Advanced Transportation

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Driver identification is an emerging area of interest in vehicle telematics, automobile control, and insurance. Recent body of works indicates that it may be possible to uniquely identify a driver using multiple dedicated sensors. In this paper, we present an approach for driver identification using smartphone GPS data alone. For our experiments, we collected data from 38 drivers for two months. We quantified the driver's natural style by extracting a set of 137 statistical features from data generated for each completed trip. The analysis shows that, for the "driver identification" problem, an average accuracy of 82.3% is achieved for driver groups of 4-5 drivers. This is comparable to the state of the arts where mostly a multisensor approach has been taken. Further, it is shown that certain behavioral attributes like high driving skill impact identification accuracy. We observe that Random Forest classifier offers the best results. These results have great implications for various stakeholders since the proposed method can identify a driver based on his/her naturalistic driving style which is quantified in terms of statistical parameters extracted from only GPS data.

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Smartphone-Based Vehicle Telematics: A Ten-Year Anniversary

Cited by 150 publications

References 237 publications

Enabling real-time road anomaly detection via mobile edge computing

Enabling real-time road anomaly detection via mobile edge computing

Socially-aware congestion control in ad-hoc networks: Current status and the way forward

Investigations on Driver Unique Identification from Smartphone’s GPS Data Alone

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