Driver Behavior Profiling Using Smartphones: A Low-Cost Platform for Driver Monitoring

Castignani, German; Derrmann, Thierry; Frank, Raphaël; Engel, Thomas

doi:10.1109/mits.2014.2328673

Cited by 277 publications

(185 citation statements)

References 11 publications

Supporting

Mentioning

170

Contrasting

Unclassified

Order By: Relevance

“…These sensors, typically threeaxis accelerometers, can accurately trace the movements made by a device. For example, in Castignani et al, 10 the authors investigate how motion sensors can be used to detect risky driving events and develop a platform for monitoring driving habits. In addition to these sensors, GPS subsystems fall into this category, because apart from allowing positioning of an object or user in space at a larger scale, they provide comprehensive travel data (e.g.…”

Section: Motion Sensor Metricsmentioning

confidence: 99%

Characterizing user mobility using mobile sensing systems

Faye

Bronzi

Tahirou

et al. 2017

International Journal of Distributed Sensor Networks

Self Cite

View full text Add to dashboard Cite

Recent technological advances and the ever-greater developments in sensing and computing continue to provide new ways of understanding our daily mobility. Smart devices such as smartphones or smartwatches can, for instance, provide an enhanced user experience based on different sets of built-in sensors that follow every user action and identify its environment. Monitoring solutions such as these, which are becoming more and more common, allows us to assess human behavior and movement at different levels. In this article, extended from previous work, we focus on the concept of human mobility and explore how we can exploit a dataset collected opportunistically from multiple participants. In particular, we study how the different sensor groups present in most commercial smart devices can be used to deliver mobility information and patterns. In addition to traditional motion sensors that are obviously important in this field, we are also exploring data from physiological and environmental sensors, including new ways of displaying, understanding, and analyzing data. Furthermore, we detail the need to use methods that respect the privacy of users and investigate the possibilities offered by network traces, including Wi-Fi and Bluetooth communication technologies. We finally offer a mobility assistant that can represent different user characteristics anonymously, based on a combination of Wi-Fi, activity data, and graph theory.

show abstract

Section: Motion Sensor Metricsmentioning

confidence: 99%

Characterizing user mobility using mobile sensing systems

Faye

Bronzi

Tahirou

et al. 2017

International Journal of Distributed Sensor Networks

Self Cite

View full text Add to dashboard Cite

show abstract

“…This framework approach wasn't genuine since it needs dynamic following of vehicle and capacity of way taken by vehicle and so on., which is the essential need to get the attempt at manslaughter driver. Driver conduct outlining framework [5] utilizes a fastidious rationale to figure the score for unique drivers by giving course topology, climate conditions, and identifies the unverifiable driving occasions and furthermore the likely dangers. So as to recognize the impact in street scenes, a calculation was produced to compute crash conceivable outcomes [6].…”

Section: Literature Surveymentioning

confidence: 99%

An Intelligent Road Traffic Control System for Passenger Safety

Kumari¹,

Reddy²

2017

Nctet-2k17

View full text Add to dashboard Cite

Abstract-Every year, there are a huge number of thruway passings and a huge number of genuine wounds because of "Keep running Off-Road" mischancse. Everything from straightforward driver absentmindedness, to exhaustion, hardness, to inebriated driving, is capable. Basic sensors can be fitted inside vehicles installed with different elements like, programmed crash warning, vehicle security, speed control which can offer catalyst to an effective street wellbeing framework. The elements that are proposed in this work are: Automatic impact notice that offers notice to the casualty's relative, Red light activity control ensures vehicle doesn't break flag, Speed control adjusts speed in various zones, Horn control averts sounding in horn precluded zone, Alcohol recognition distinguishes intoxicated driving and Vehicle security is utilized to counteract burglary.

show abstract

“…Today's smartphones include inertial sensors that enable smartphone driver support systems (SDSS) to detect driving events, such as acceleration, braking, turning and lane changing [8] [10] [28] [29] [30]. SDSS typically score a driver's behavior [8] or classify it in some way, e.g.…”

Section: Related Work: Monitoring Driver Behaviormentioning

confidence: 99%

“…SDSS typically score a driver's behavior [8] or classify it in some way, e.g. passive or aggressive, risky or safe [31].…”

Section: Related Work: Monitoring Driver Behaviormentioning

confidence: 99%

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

Warren¹,

Meads²,

Whittaker³

et al. 2017

Preprint

View full text Add to dashboard Cite

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.

show abstract

Driver Behavior Profiling Using Smartphones: A Low-Cost Platform for Driver Monitoring

Cited by 277 publications

References 11 publications

Characterizing user mobility using mobile sensing systems

Characterizing user mobility using mobile sensing systems

An Intelligent Road Traffic Control System for Passenger Safety

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

Contact Info

Product

Resources

About