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

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.

Driving pattern analysis to determine driver behaviors for local authority based on cloud using OBD II

“…Volume 13, Number 10,2022 The direct association of driving behaviour with the driving style should be examined because it affects a vehicle's fuel consumption, emission, and safety. The OBD [1] helps to collect real-time data for post-analysis.…”

“…Pan et al [9] used the logistic regression model and examined behavioural parameters that affect a vehicle's risk situation and factors that affect safe driving. Ameen et al [10] determined driving data, such as speed, rpm, and throttle position, by using OBD II. They proposed a method to develop a driving behaviour classification by using severity stratification of the acceleration model to determine safe driving.…”

Influence of the Weld Joint Position on the Mechanical Stress Concentration in the Construction of the Alternative Skid Car System’s Skid Chassis

“…Nevertheless, it is not excluded that the combination of the human factor (portrayed by the driver) and autonomous vehicles on real road traffic can be of great help in a condition that carries a significant risk of collision [1]. Therefore, it is necessary to develop new or to improve existing car control systems [2][3][4][5][6][7][8], with the help of both existing data, and the theories and tools at our disposal, and proceed with the verification and validation of results compared to those obtained experimentally in laboratory conditions. This applied approach results in a rapid development while maintaining financial efficiency, unlike in cases in which fundamental research requires substantial financial resources.…”