Validation study of risky event classification using driving pattern factors

Abstract-Just like it has irrevocably reshaped social life, the fast growth of smartphone ownership is now beginning to revolutionize the driving experience and change how we think about automotive insurance, vehicle safety systems, and traffic research. This paper summarizes the first ten years of research in smartphone-based vehicle telematics, with a focus on userfriendly implementations and the challenges that arise due to the mobility of the smartphone. Notable academic and industrial projects are reviewed, and system aspects related to sensors, energy consumption, cloud computing, vehicular ad hoc networks, and human-machine interfaces are examined. Moreover, we highlight the differences between traditional and smartphonebased automotive navigation, and survey the state-of-the-art in smartphone-based transportation mode classification, driver classification, and road condition monitoring. Future advances are expected to be driven by improvements in sensor technology, evidence of the societal benefits of current implementations, and the establishment of industry standards for sensor fusion and driver assessment.

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“…[259] x x x x x Symbolic aggregate approximation. [260] x x x x x Maximum likelihood estimation. [174] x x x x x x NBC.…”

Section: Driver Behavior Classificationmentioning

confidence: 99%

Smartphone-Based Vehicle Telematics: A Ten-Year Anniversary

Wahlström

Skog

Händel

2017

IEEE Trans. Intell. Transport. Syst.

157

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“…For classifying driving events or maneuvers, previous works use different types of sensors. Castignani et al [ 6 ] suggests combining multiple sensors and GPS information obtained from a smartphone to detect unusual driving events (acceleration, braking and cornering maneuvers), in order to provide a final driving score to drivers. They create an adaptive driver profiling method, which relies on a multivariate normal distribution (MVN), to build a statistical model of a user’s driving characteristics.…”

Section: Related Workmentioning

confidence: 99%

Estimating Vehicle Movement Direction from Smartphone Accelerometers Using Deep Neural Networks

Sánchez

Pozo

Gómez

2018

Sensors

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Characterization of driving maneuvers or driving styles through motion sensors has become a field of great interest. Before now, this characterization used to be carried out with signals coming from extra equipment installed inside the vehicle, such as On-Board Diagnostic (OBD) devices or sensors in pedals. Nowadays, with the evolution and scope of smartphones, these have become the devices for recording mobile signals in many driving characterization applications. Normally multiple available sensors are used, such as accelerometers, gyroscopes, magnetometers or the Global Positioning System (GPS). However, using sensors such as GPS increase significantly battery consumption and, additionally, many current phones do not include gyroscopes. Therefore, we propose the characterization of driving style through only the use of smartphone accelerometers. We propose a deep neural network (DNN) architecture that combines convolutional and recurrent networks to estimate the vehicle movement direction (VMD), which is the forward movement directional vector captured in a phone’s coordinates. Once VMD is obtained, multiple applications such as characterizing driving styles or detecting dangerous events can be developed. In the development of the proposed DNN architecture, two different methods are compared. The first one is based on the detection and classification of significant acceleration driving forces, while the second one relies on longitudinal and transversal signals derived from the raw accelerometers. The final success rate of VMD estimation for the best method is of 90.07%.

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“…We can find the list of selected variables and a brief description of each one in Table 1. PKE and RPA are parameters related to the eco-driving and that can also be related to the aggressiveness of driving [8], [9].…”

Section: B Features Selection 1) Shannon Entropymentioning

confidence: 99%

Determination of the electric vehicles driving modes in real life conditions by classification methods

Ben-Marzouk

Carrault

Pélissier

et al. 2018

2018 IEEE International Conference on Industrial Technology (ICIT)

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In order to study the aging of batteries in automotive applications, it is important to understand how and under what conditions these batteries are operating in electric vehicles (EVs). However, because of the specificities of EVs, these uses may be very different from those known from internal combustion engine cars (ICE). In this paper, we present how, from real-life data, we determine the different driving modes of electric vehicles. The paper presents the different techniques adopted to analyze and classify the data and the different EV running modes, which are obtained.

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Validation study of risky event classification using driving pattern factors

Cited by 7 publications

References 13 publications

Smartphone-Based Vehicle Telematics: A Ten-Year Anniversary

Smartphone-Based Vehicle Telematics: A Ten-Year Anniversary

Estimating Vehicle Movement Direction from Smartphone Accelerometers Using Deep Neural Networks

Determination of the electric vehicles driving modes in real life conditions by classification methods

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