Safe Driving Using Mobile Phones

Fazeen, Mohamed; Gozick, Brandon; Dantu, Ram; Bhukhiya, Moiz; González, Marta C.

doi:10.1109/tits.2012.2187640

Cited by 296 publications

(144 citation statements)

References 16 publications

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“…In addition to the driver behavior monitoring feature, Fazeen et al [33] also added a road condition characterization and mapping feature to their system that uses a smartphone's GPS and accelerometer.…”

Section: Environmental Informationmentioning

confidence: 99%

See 1 more Smart Citation

Survey of smartphone‐based sensing in vehicles for intelligent transportation system applications

Engelbrecht

Booysen

Rooyen

et al. 2015

IET Intelligent Transport Systems

158

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Road crashes are a growing concern of governments and is rising to become one of the leading preventable causes of death, especially in developing countries. The ubiquitous presence of smartphones provides a new platform on which to implement sensor networks and driver assistance systems, as well as other ITS applications. In this paper, existing approaches of using smartphones for ITS applications are analyzed and compared. Particular focus is placed on vehicle-based monitoring systems, such as driving behavior and style recognition, accident detection and road condition monitoring systems. Further opportunities for use of smartphones in ITS systems are highlighted, and remaining challenges in this emerging field of research are identified.

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Section: Environmental Informationmentioning

confidence: 99%

“…The road condition monitoring feature of Mohan et al [34] is more accurate than that of Fazeen et al [33], and its implementation is simpler. Regarding driver behavior monitoring -the drunk driving detection system of Dai et al [32] is the most accurate, achieving a FN rate of 0%.…”

Section: Accuracy Versus Simplicitymentioning

confidence: 99%

Survey of smartphone‐based sensing in vehicles for intelligent transportation system applications

Engelbrecht

Booysen

Rooyen

et al. 2015

IET Intelligent Transport Systems

158

View full text Add to dashboard Cite

show abstract

“…Firstly, obtaining a gravity vector with the vehicle stopped and, secondly, obtaining a driving directional vector and correcting it by a Principal Component Analysis (PCA) approach. Other authors try to use a fixed Inertial Measurement Unit on the vehicle [12] or they use a smartphone in a fixed and calculated position in [13,14].…”

Section: Introductionmentioning

confidence: 99%

Modelling the Effect of Driving Events on Electrical Vehicle Energy Consumption Using Inertial Sensors in Smartphones

et al. 2018

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Air pollution and climate change are some of the main problems that humankind is currently facing. The electrification of the transport sector will help to reduce these problems, but one of the major barriers for the massive adoption of electric vehicles is their limited range. The energy consumption in these vehicles is affected, among other variables, by the driving behavior, making range a value that must be personalized to each driver and each type of electric vehicle. In this paper we offer a way to estimate a personalized energy consumption model by the use of the vehicle dynamics and the driving events detected by the use of the smartphone inertial sensors, allowing an easy and non-intrusive manner to predict the correct range for each user. This paper proposes, for the classification of events, a deep neural network (Long-Short Time Memory) which has been trained with more than 22,000 car trips, and the application to improve the consumption model taking into account the driver behavior captured across different trips, allowing a personalized prediction. Results and validation in real cases show that errors in the predicted consumption values are halved when abrupt events are considered in the model.

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“…These capabilities form a foundation for monitoring and analyzing driver behavior, e.g. [7] [8] [9] [10]. Applications of the technology include insurance telematics (Pay How You Drive) [11], detecting impaired driving [12] [13], car pooling and ride sharingwhere driver reputation and safety are used to decide who to drive with, eco-driving [14] to reduce pollution, and use of crowd-sourced data to identify potential crashrisk areas of the road network [15].…”

Section: Introductionmentioning

confidence: 99%

Behavior Change for Youth Drivers: Design and Development of a Smartphone-Based App (BackPocketDriver) (Preprint)

Warren¹,

Meads²,

Whittaker³

et al. 2017

Preprint

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Background: The over representation of youth in road crash injury and fatality rates is a major public health problem globally. In New Zealand, youth drivers are most vulnerable in the restricted license period, when they can drive without the requirement for supervision by an experienced adult. Behavioral change interventions delivered using smartphone technology to young drivers could serve as a useful feedback mechanism for safe driving in this context, but this potential remains to be fully explored. Objective: We have applied behavioral change principles to design and develop a smartphone based intervention with the aim of helping youth drivers develop and hone safe driving skills. Methods: An iterative process was used to support development of the smartphone intervention. We reviewed behavioral change literature, identifying fundamental principles and exploring use of behavioral change techniques in other areas of public health. We engaged with key stakeholders, including young drivers, government agencies and relevant organizations. We also took into account technology adoption considerations when designing the application. Results: We developed BackPocketDriver, an Android smartphone app that uses inbuilt sensors to monitor and infer driver behavior. The application implements features that were identified during the design process and which are traceable to behavioral change techniques. Features identified include messaging to enhance motivation and driver education, journey feedback summaries, goal setting, social support and gamification elements (achievements and leaderboards). Conclusions: BackPocketDriver's design rests on a sound foundation -behavioral change theory. With explicit links between the theory and app features, the app aims to be an effective intervention to change and improve youth driver behavior. The next phase of this work is to run a small pilot study to assess BackPocketDriver's effectiveness.

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Safe Driving Using Mobile Phones

Cited by 296 publications

References 16 publications

Survey of smartphone‐based sensing in vehicles for intelligent transportation system applications

Survey of smartphone‐based sensing in vehicles for intelligent transportation system applications

Modelling the Effect of Driving Events on Electrical Vehicle Energy Consumption Using Inertial Sensors in Smartphones

Behavior Change for Youth Drivers: Design and Development of a Smartphone-Based App (BackPocketDriver) (Preprint)

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