A smartphone-based sensing platform to model aggressive driving behaviors

Abstract

Driving aggressively increases the risk of accidents. Assessing a person's driving style is a useful way to guide aggressive drivers toward having safer driving behaviors. A number of studies have investigated driving style, but they often rely on the use of self-reports or simulators, which are not suitable for the real-time, continuous, automated assessment and feedback on the road. In order to understand and model aggressive driving style, we construct an invehicle sensing platform that uses a smartphone instead of using heavyweight, expensive systems. Utilizing additional cheap sensors, our sensing platform can collect useful information about vehicle movement, maneuvering and steering wheel movement. We use this data and apply machine learning to build a driver model that evaluates drivers' driving styles based on a number of driving-related features. From a naturalistic data collection from 22 drivers for 3 weeks, we analyzed the characteristics of drivers who have an aggressive driving style. Our model classified those drivers with an accuracy of 90.5% (violation-class) and 81% (questionnaire-class). We describe how, in future work, our model can be used to provide real-time feedback to drivers using only their current smartphone.

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

“…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.…”

“…To understand and model reckless driving behavior, Hong, Margines and Dey [56] implemented a low-cost, in-vehicle sensing platform. Unlike Zhao et al [55], which only uses a smartphone's sensors, this platform added an OBD-II diagnostic device to collect data from the vehicle, such as speed, rpm, speed, and throttle position.…”

“…As in the work proposed by Zhao et al [55], analysis of driver behavior is off-line. However, Hong, Margines and Dey [56], templates must be stored, such as acceleration, deceleration, and curves, which are used for comparison with the driver's maneuvers and a subsequent classification as cautious or reckless. According to Banovic et al [73], this is a weakness because machine learning algorithms classify and predict only the most frequent behaviors.…”

“…The Join Driving scoring mechanism was implemented according to Zhao et al [55]. Finally, a machine learning model (naïve Bayes classifier) [56] using data from smartphone and OBD-II device was implemented. The performance evaluation process of each approach followed the steps described in section 6.1.…”

Risky Gadgets to the Rescue - Reframing In-Car Technology Use as Task Engagement

“…Dashboard or action cameras could potentially extend these capabilities. Hong et al (2014) constructed a platform consisting of a smartphone and cheap sensors that assesses aggressive driving style. While this is not a heavyweight, expensive setup, it still requires additional components that are neither part of the car itself or the driver's everyday belongings.…”