Fine-Grained Abnormal Driving Behaviors Detection and Identification with Smartphones

Yu, Jiadi; Chen, Zhongyang; Zhu, Yanmin; Chen, Yingying; Kong, Linghe; Li, Minglu

doi:10.1109/tmc.2016.2618873

Cited by 126 publications

(67 citation statements)

References 17 publications

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“…Yu et al [15] have proposed a system called "Fine-grained abnormal driving behavior detection and identification system, D3" to detect real-time high-accurate abnormal driving behavior. SVM and Neural Network algorithms are used to detect the abnormality.…”

Section: Pattern Monitoring Using Mobile Phone Sensorsmentioning

confidence: 99%

“…The research papers [11][12][13][14] focused on providing personalized premium based on the driving behavior. The research papers [15][16][17][18] have used machine learning, big data and deep learning algorithms to classify the driving behavior. The research papers [20,23] have suggested to consider other factors apart from behavioral factors to detect driving behavior.…”

Section: Pattern Monitoring Using Obdmentioning

confidence: 99%

See 1 more Smart Citation

A survey on driving behavior analysis in usage based insurance using big data

Arumugam

Bhargavi

2019

J Big Data

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An accident is defined as an unfortunate incident that happens unexpectedly and unintentionally, typically resulting in damage or injury. Considering all the consequences that could eventuate after an accident, there are reasons to believe that a normal person does not drive with an ex-ante intention to cause an accident. Holding a valid driving license is prerequisite to drive in any part of the world and during the licensing

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Section: Pattern Monitoring Using Mobile Phone Sensorsmentioning

confidence: 99%

Section: Pattern Monitoring Using Obdmentioning

confidence: 99%

A survey on driving behavior analysis in usage based insurance using big data

Arumugam

Bhargavi

2019

J Big Data

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“…Earlier inertia-sensor-based safe driving monitoring systems mainly focus on monitoring car motion dynamics and can alert abnormal vehicle movements, such as aggressive acceleration or turning [9,38,72,74,75]. However, they often leave insufficient time for the drivers to respond to complex road situations, especially when the drivers are distracted.…”

Section: State Of the Artmentioning

confidence: 99%

MagTrack

Huang

Chen

Lin

2019

Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services

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Hands on the wheel, eyes on the road" is the central guideline of safe vehicle driving practices. Many advanced driver assistance systems can effectively detect abnormal vehicle motions. However, these systems often leave insufficient time for drivers to respond to complex road situations, especially when the drivers are distracted. To reduce accidents, it is essential to detect whether a driver complies with safe driving guidelines in real time and provide warnings early before any dangerous maneuvers occur. There are vision-based driver distraction monitoring systems which rely on cameras in high-end vehicles, but their performances are heavily constrained by visibility requirements. In this paper, we present MagTrack, a driver monitoring system that is based on tracking magnetic tags worn by the user. With a single smartwatch and two low-cost magnetic accessories: a hand magnetic ring and a head magnetic eyeglasses clip, our system tracks and classifies a driver's bimanual and head movements simultaneously using both analytical and approximation sensing models. Our approach is robust to driver's postures, vehicles, and environmental changes. We demonstrate that a wide range of activities can be detected by our system, including bimanual steering, visual and manual distractions, and lane changes and turns. In extensive road tests with 500+ instances of driving activities and 500+ minutes of road driving with 10 subjects, MagTrack achieves 87% of precision and 90% of recall rate on the detection of unsafe driving activities. CCS CONCEPTS• Human-centered computing → Ubiquitous and mobile computing.

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“…Though the method of Yu et al [19] obtains a high prediction accuracy, i.e., 96.88%, its feature set is quite big, i.e., 152 features. This factor absolutely leads to a high computational time and resource.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Basic Activity Sequence Matching Method in Abnormal Driving Pattern Detection Using Smartphone Sensors

Nguyen

Lu²,

Nguyen

2020

Electronics

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In this work, we present a novel method, namely dynamic basic activity sequence matching (DAS), a combination of machine learning methods and flexible threshold based methods for distinguishing normal and abnormal driving patterns. Indeed, DAS relies on the activity detection module (ADM) presented in our previous work to analyze each driving pattern as a sequence of basic activities-stopping (S), going straight (G), turning left (L), and turning right (R). In fact, the threshold value and other parameters like the duration of long and short activities are iteratively induced from the collected dataset. Hence, DAS is flexible and independent of driving contexts such as vehicle modes and road conditions. Experimental results, on the dataset collected from numerous motorcyclists, show the outperformance of our proposed method against dynamic time warping and the two popular machine learning methods-random forest and neural network-in distinguishing the normal and abnormal driving patterns. Moreover, we propose an efficient framework composing of two phases: in the first phase, the normal and abnormal driving patterns are distinguished by relying on DAS. In the second phase, the detected abnormal patterns are further classified into various specific abnormal driving patterns-weaving, sudden braking, etc. This fusion framework again achieves the highest overall accuracy of 97.94%.

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Fine-Grained Abnormal Driving Behaviors Detection and Identification with Smartphones

Cited by 126 publications

References 17 publications

A survey on driving behavior analysis in usage based insurance using big data

A survey on driving behavior analysis in usage based insurance using big data

MagTrack

Dynamic Basic Activity Sequence Matching Method in Abnormal Driving Pattern Detection Using Smartphone Sensors

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