Driving style recognition using a smartphone as a sensor platform

By Derick A. Johnson, Mohan Manubhai Trivedi. 2011

751 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Automotive Engineering, Signal Processing

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Citations in the corpus

A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, background): “In [229]–[231] driving style was categorized into three and into two in [74], [209], [217], [218], [222].”
Fusion of Smartphone Motion Sensors for Physical Activity Recognition (2014, positive): “Moreover, an accelerometer and a gyroscope were combined to detect certain dangerous driving behaviors in [15,16].”
Driving Behavior and Traffic Safety: An Acceleration-Based Safety Evaluation Procedure for Smartphones (2014, background): “…in scientific literature as the most significant for a quantitative evaluation of unsafe or aggressive driving style; these parameters are the longitudinal and the lateral accelerations and decelerations (Shaout & Bodenmiller, 2011; Klauer et al., 2009; Johnson et al., 2011; Paefgen et al., 2012).”
UTiLearn: A Personalised Ubiquitous Teaching and Learning System for Smart Societies (2017, background): “It can also help to predict various human behaviors such as rash driving styles [85] and emergency situations in advance like aggressive actions which can lead to violence [86] using smartphone based sensors.”
Estimating driving behavior by a smartphone (2012, background): “The energy scores beyond the threshold range are eliminated.”
Driving style classification using long-term accelerometer information (2014, methodology): “Trivedi [8] created the system called MIROAD that uses Dynamic Time Warping (DTW) and smartphone based sensor-fusion (accelerometer, gyroscope, magnetometer, GPS,”
Online driving style recognition using fuzzy logic (2014, background): “Johnson and Trivedi use a smartphone to identify the driving style in [6].”
Driver Behavior Profiling Using Smartphones: A Low-Cost Platform for Driver Monitoring (2015, methodology): “[12] also proposed a DTW-based driver profile algorithm, MIROAD, using smartphone sensors, GPS and camera.”
A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities (2019, methodology): “Furthermore, if used in pair with the gyroscope, sensing readings from the accelerometers enable to monitor the user driving style [79].”