Driving style recognition using a smartphone as a sensor platform

Johnson, Derick A.; Trivedi, Mohan M.

doi:10.1109/itsc.2011.6083078

Cited by 596 publications

(348 citation statements)

References 20 publications

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“…Derick A. Johnson MIROAD considers the use of smartphones to find out the driving style which can be put into two categories: Aggressive and Non-aggressive [2].…”

Section: Literature Reviewmentioning

confidence: 99%

Survey on User’s Micro-Environment control using Smartphone Sensors in Android

Jamaiwar¹,

Surve²,

Punde³

et al. 2018

IJSRCSE

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Abstract-Context-awareness is obtaining more and more necessary for a variety of mobile and pervasive applications on these days good phones. Whereas human-centric contexts (e.g., indoor/ out of doors, at home/in workplace, driving/walking) are extensively researched, few makes an attempt have studied from phones perspective (e.g., on table/sofa, in pocket/bag/hand). We have a tendency to see such immediate surroundings as micro atmosphere, typically many to a dozen of centimetres, around a phone. In this study, we have a tendency to style and implement micro-environment sensing platform that mechanically records detector hints and characterizes the micro-environment of good phones. The platform runs as a daemon method on a wise phone and provides finer-grained atmosphere data to higher layer applications via programming interfaces.

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“…Derick A. Johnson MIROAD considers the use of smartphones to find out the driving style which can be put into two categories: Aggressive and Non-aggressive [2].…”

Section: Literature Reviewmentioning

confidence: 99%

Survey on User’s Micro-Environment control using Smartphone Sensors in Android

Jamaiwar¹,

Surve²,

Punde³

et al. 2018

IJSRCSE

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show abstract

“…Having to place the smartphone in a fixed position within the vehicle is also a limitation of the system. No data is provided regarding the power usage of the system, but the lack of GPS sampling and the simple Bayesian classifier used is indicative of a slightly lower power consumption than the MIROAD [9] system .…”

Section: Estimating Driver Behavior By a Smartphone -Eren Et Almentioning

confidence: 99%

“…This is achieved by detecting and positively identifying a combination of dangerous driving maneuvers associated with drunk driving. Johnson and Trivedi's [9] system can detect and identify a number of different driving maneuvers, but does not draw any conclusions from them. Their intent is to use the system to support a holistic driver assistance system (DAS) by providing it with additional information.…”

Section: Driving Maneuver Recognition Versus Driving Behavior Classifmentioning

confidence: 99%

“…The computing power and efficiency of modern smartphones, however, has increased dramatically, which provides headroom for more complex solutions. Therefore there is still merit in implementing a more complex approach as used by Johnson and Trivedi [9] -if the accuracy could be improved to such an extent as to have no false negatives or positives whatsoever.…”

Section: Accuracy Versus Simplicitymentioning

confidence: 99%

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

Engelbrecht

Booysen

Rooyen

et al. 2015

IET Intelligent Transport Systems

152

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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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“…Johnson et al [8] detected and classified driving maneuvers using a smartphone's accelerometer and gyro sensors mounted in the car. Subsequent research has focus on implementing these systems on a smartphone.…”

Section: A Driving Event Detection and Classification Using Inertialmentioning

confidence: 99%

Driver classification and driving style recognition using inertial sensors

Ly¹,

Martin²,

Trivedi³

2013

2013 IEEE Intelligent Vehicles Symposium (IV)

Self Cite

219

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Abstract-Currently there are many research focused on using smartphone as a data collection device. Many have shown its sensors ability to replace a lab test bed. These inertial sensors can be used to segment and classify driving events fairly accurately. In this research we explore the possibility of using the vehicle's inertial sensors from the CAN bus to build a profile of the driver to ultimately provide proper feedback to reduce the number of dangerous car maneuver. Braking and turning events are better at characterizing an individual compared to acceleration events. Histogramming the time-series values of the sensor data does not help performance. Furthermore, combining turning and braking events helps better differentiate between two similar drivers when using supervised learning techniques compared to seperate events alone, albeit with anemic performance.

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Driving style recognition using a smartphone as a sensor platform

Cited by 596 publications

References 20 publications

Survey on User’s Micro-Environment control using Smartphone Sensors in Android

Survey on User’s Micro-Environment control using Smartphone Sensors in Android

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

Driver classification and driving style recognition using inertial sensors

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