Identification of Driving Safety Profiles in Vehicle to Vehicle Communication System Based on Vehicle OBD Information

Hussein Ali Ameen,Sharifah Saon,Zahraa Hashim Kareem,Abd Kadir Mahamad,Shingo Yamaguchi,Rami Qays Malik,Mohd Anuaruddin Bin Ahmadon

doi:10.3390/info12050194

Hussein Ali Ameen, Sharifah Saon + Show 5 more

Open Access

https://doi.org/10.3390/info12050194

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Abstract

Driver behavior is a determining factor in more than 90% of road accidents. Previous research regarding the relationship between speeding behavior and crashes suggests that drivers who engage in frequent and extreme speeding behavior are overinvolved in crashes. Consequently, there is a significant benefit in identifying drivers who engage in unsafe driving practices to enhance road safety. The proposed method uses continuously logged driving data to collect vehicle operation information, including vehicle speed, engine revolutions per minute (RPM), throttle position, and calculated engine load via the on-board diagnostics (OBD) interface. Then the proposed method makes use of severity stratification of acceleration to create a driving behavior classification model to determine whether the current driving behavior belongs to safe driving or not. The safe driving behavior is characterized by an acceleration value that ranges from about ±2 m/s2. The risk of collision starts from ±4 m/s2, which represents in this study the aggressive drivers. By measuring the in-vehicle accelerations, it is possible to categorize the driving behavior into four main classes based on real-time experiments: safe drivers, normal, aggressive, and dangerous drivers. Subsequently, the driver’s characteristics derived from the driver model are embedded into the advanced driver assistance systems. When the vehicle is in a risk situation, the system based on nRF24L01 + power amplifier/low noise amplifier PA/LNA, global positioning system GPS, and OBD-II passes a signal to the driver using a dedicated liquid-crystal display LCD and light signal. Experimental results show the correctness of the proposed driving behavior analysis method can achieve an average of 90% accuracy rate in various driving scenarios.

Highlights

Accidents are considered one of the most detrimental aspects of the usage of automobiles
The data exchange in V2V is measured using several vehicles with various locations and scenarios based on the nRF24L01 + PA/LNA wireless transceiver module with an external antenna and the Arduino Uno board connected to collect the data in each experiment scenario
The two devices were connected via seven wires: power supply (3.3VCC), ground (GND), chip select not (CSN), chip enable (CE), serial clock (SCK), master in, slave out (MISO), and master out, slave in (MOSI)- attached to PCs through a USB cable in both the transmitter and receiver

Summary

Introduction

Accidents are considered one of the most detrimental aspects of the usage of automobiles. Due to the high density of vehicles, potential threats and road accidents increase [1]. Road accidents are of great importance to all humanity, and this is a challenging issue that needs to be considered [2]. These accidents stem from driver errors, such as speeding and aggressive driving [3]. Due to the ephemeral nature of Vehicle-to-Vehicle (V2V) networks, no individual vehicle can have a good view of the other vehicle’s incident report behavior to reason about trustworthiness effectively. The situation analysis reports that major casualties are due to the improper driving behavior of drivers. In 24% of the cases the unexpected behavior is the cause of the accident and only 0.7% of accidents are due to technological failures (Figure 1) [3,5,6,7]

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