Automatic Identification of Driver’s Smartphone Exploiting Common Vehicle-Riding Actions

Park, Ho-Min; Ahn, DaeHan; Park, Taejoon; Shin, Kang G.

doi:10.1109/tmc.2017.2724033

Cited by 22 publications

(18 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, we can safely ignore such a case since drivers will not be able to manipulate the phone in the back seat. Lastly, remote vehicle starters are not considered in this paper since most vehicles are not equipped with such an option [9].…”

Section: The Proposed Systemmentioning

confidence: 99%

See 1 more Smart Citation

Accurate Driver Detection Exploiting Invariant Characteristics of Smartphone Sensors

Ahn

Park

Shin

et al. 2019

Sensors

Self Cite

View full text Add to dashboard Cite

Distracted driving jeopardizes the safety of the driver and others. Numerous solutions have been proposed to prevent distracted driving, but the number of related accidents has not decreased. Such a deficiency comes from fragile system designs where drivers are detected exploiting sensory features from strictly controlled vehicle-riding actions and unreliable driving events. We propose a system called ADDICT (Accurate Driver Detection exploiting Invariant Characteristics of smarTphone sensors), which identifies the driver utilizing the inconsistency between gyroscope and magnetometer dynamics and the interplay between electromagnetic field emissions and engine startup vibrations. These features are invariantly observable regardless of smartphone positions and vehicle-riding actions. To evaluate the feasibility of ADDICT, we conducted extensive experiments with four participants and three different vehicles by varying vehicle-riding scenarios. Our evaluation results demonstrated that ADDICT identifies the driver’s smartphone with 89.1% average accuracy for all scenarios and >85% under the extreme scenario, at a marginal cost of battery consumption.

show abstract

Section: The Proposed Systemmentioning

confidence: 99%

“…Second, the sensory features are not guaranteed to be found in a timely manner, thus failing to protect the user from the very beginning. Although there exist several studies addressing this issue by identifying the driver before the vehicle leaves its parking spot [8,9], they still need to address the first problem.…”

Section: Introductionmentioning

confidence: 99%

Accurate Driver Detection Exploiting Invariant Characteristics of Smartphone Sensors

Ahn

Park

Shin

et al. 2019

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…TEXIVE is very accurate that the dangerous driving behaviours are identified without any extra devices rather than smart phone. This paper [11], gives the solution to the problem of distracted driving by giving a eventdriven solution called as Automatic Identification of Driver's Smart phone (AIDS). It makes use of features that are available for identifying the driver's phone.…”

Section: Litrature Surveymentioning

confidence: 99%

A Survey Paper on Non Intrusive Texting While Driving Detection Using Smart Phone

2018

IJMTER

View full text Add to dashboard Cite

Abstract-Many interesting systems on mobile phone are introduced to prevent the dangerous activity of texting while driving (T&D). However these systems require user's manual input to block the texting activity or some extra devices are used to identify user's location and also some localization techniques are used. Here the idea is to propose a method which is able to detect T&D without any user intervention. To implement this idea various smart phone embedded sensors such as gyroscopes, accelerometers and GPS are used. They are used to collect the information such as touch strokes, holding orientation and vehicle speed. Using these patterns the whether the T&D is existing or not identified.

show abstract

“…In these situations, we need to recognize the motors. However, it is difficult for us to extract the pictures of the front of the motors from the camera [1]. But it is easier for us to get the images of the side view of the motors.…”

Section: Introductionmentioning

confidence: 99%

Motor Side-View Recognition System Based on Wavelet Entropy and Naive Bayesian Classifier

Chen¹,

Сучков²

2018

Proceedings of the 2018 3rd International Conference on Electrical, Automation and Mechanical Engineering (EAME 2018)

View full text Add to dashboard Cite

As the traffic accident becomes a serious problem, we need some more efficient methods to identify the car. Luckily, we can use artificial intelligence to recognize the motors by using side-view images. It will help a lot to solve the traffic accident. We used the wavelet entropy to extract the feature of the images. Then we employed the naïve Bayesian theory as the classifier. And we used 10-fold cross validation in our experiment. We used a three-level decomposition for WE. It got an overall accuracy of 75% in recognizing motors. In the future we will try to improve the accuracy of this method and try to identify cars form different brand.

show abstract

Automatic Identification of Driver’s Smartphone Exploiting Common Vehicle-Riding Actions

Cited by 22 publications

References 18 publications

Accurate Driver Detection Exploiting Invariant Characteristics of Smartphone Sensors

Accurate Driver Detection Exploiting Invariant Characteristics of Smartphone Sensors

A Survey Paper on Non Intrusive Texting While Driving Detection Using Smart Phone

Motor Side-View Recognition System Based on Wavelet Entropy and Naive Bayesian Classifier

Contact Info

Product

Resources

About