DriveSafe: An app for alerting inattentive drivers and scoring driving behaviors

Bergasa, Luis M.; Almeria, Daniel; Almazán, Javier; Yebes, J. Javier; Arroyo, Roberto

doi:10.1109/ivs.2014.6856461

Cited by 164 publications

(132 citation statements)

References 15 publications

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“…The paper [10] presents a DriveSafe driver safety application for iPhones that detects inattentive driving behaviors and gives corresponding feedback to drivers, scoring their driving and alerting them in case their behaviors are unsafe. It uses computer vision and pattern recognition techniques on the iPhone to assess whether the driver is drowsy or distracted using the rear-camera, the microphone, the inertial sensors and the GPS.…”

Section: Related Workmentioning

confidence: 99%

Impact of the face angle to traveling trajectory during the riding standing-type personal mobility device

et al. 2018

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Abstract. In this paper, we investigate the impact of face direction during traveling by Standing-Type Personal Mobility Device (PMD). The use of PMD devices has been a popular choice for recreational activities in the developed countries such as in the USA and the countries in Europe. These devices are not completely risk free and various accidents have been reported. Since that, the risk factors leading to accidents have to be investigated. Unfortunately, the research studies on the risk factors on riding PMD devices have not been matured as much as the studies on driving cars. In this paper, we evaluate the impacts of face angle on travelling trajectory during travelling in a PMD. We showed by experiments that, the face direction is an important factor in risk assessment for traveling by a PMD.

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Section: Related Workmentioning

confidence: 99%

Impact of the face angle to traveling trajectory during the riding standing-type personal mobility device

et al. 2018

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“…It performs the pattern recognition research by combining the inter-axial data from multiple sensors into a single one that is single classifier. Drive safe [4], this application uses computer vision and pattern recognition techniques to alert during their unsafe driving behaviour. The inbuilt sensors present are able to detect in attentive driving behaviours and also evaluating the quality of driving at the same time.…”

Section: Litrature Surveymentioning

confidence: 99%

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

2018

IJMTER

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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.

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“…By contrast, studies have proposed the use of smartphones to understand and evaluate a driver's behavior [5,26,49,[53][54][55][56][57]. The choice of using a smartphone is made due to its affordability and wide adoption, sufficient storage capacity and processing power, as well as its equipment with a variety of sensors.…”

Section: Motivating Scenariomentioning

confidence: 99%

“…To train the algorithms, we used the open dataset provided by Bergasa et al [57]. The dataset provides three axis accelerometer data labeled as cautious and reckless based on thresholds given by Paefgen et al [54] for acceleration, braking, and turning.…”

Section: Preparationmentioning

confidence: 99%

Smartphone-based outlier detection: a complex event processing approach for driving behavior detection

Vasconcelos

Olivieri

et al. 2017

J Internet Serv Appl

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The majority of fatal car crashes are caused by reckless driving. With the sophistication of vehicle instrumentation, reckless maneuvers, such as abrupt turns, acceleration, and deceleration, can now be accurately detected by analyzing data related to the driver-vehicle interactions. Such analysis usually requires very specific in-vehicle hardware and infrastructure sensors (e.g. loop detectors and radars), which can be costly. Hence, in this paper, we investigated if off-the-shelf smartphones can be used to online detect and classify the driver's behavior in near real-time. To do so, we first modeled and performed an intrinsic evaluation to assess the performance of three outlier detection algorithms formulated as a data stream processing network which receives as input and processes data streams of smartphone and vehicle sensors. Next, we implemented a novel scoring mechanism based on online outlier detection to quantitatively evaluate drivers' maneuvers as either cautious or reckless. Thus, we adapted a data mining mechanism which takes into account a sensor's data rates and power to determine driver behavior in the scoring process. Finally, as the intrinsic evaluation does not necessarily reveal how well an algorithm will perform in a real-world scenario, we evaluated the algorithm that achieved the best result in a real-world case study to assess drivers' driving behavior. Our results indicate that the algorithm performs quickly and accurately; the algorithm classifies driver behavior with 95.45% accuracy. Moreover, such results are obtained within 100 milliseconds of processing time on average.

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DriveSafe: An app for alerting inattentive drivers and scoring driving behaviors

Cited by 164 publications

References 15 publications

Impact of the face angle to traveling trajectory during the riding standing-type personal mobility device

Impact of the face angle to traveling trajectory during the riding standing-type personal mobility device

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

Smartphone-based outlier detection: a complex event processing approach for driving behavior detection

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