Abstract:
"The rising number of road traffic accidents resulting in injury or death of
passengers has become a major concern. One of the potential causes of these accidents
is mechanical failures in vehicles. In 2016, the number of deaths caused by road
crashes reached 1.35 million, which is equivalent to 3700 deaths per day. To address
this issue, it is crucial to proactively identify vehicle engine defects before they lead
to accidents. The current method of identifying defects after they occur is not
effective, as it might be too late to prevent the accident.
The proposed solution to reduce the risk of road traffic accidents caused by
mechanical failures in vehicles is to implement an automatic defect detection system.
The system will use data analysis to predict defects before they occur, allowing users
to minimize 70% of the risk and save time and cost. By identifying issues early, users
could prevent dangerous situations and reduce the likelihood of road accidents caused
by common vehicle defects.
A defect is an uncommon, infrequent occurrence. So, a classifier or a machine
learning model that can tell the difference between normal and irregular behavior is
learned by using the OBD II device by collecting information from the computer
system of a vehicle. The proposed system will utilize advanced data analysis and
machine learning techniques, such as deep learning, to process and analyze the data,
and identify patterns and anomalies that may indicate a potential engine defect thereby
improving road safety and saving lives. In this case according to the current model the
system was able to obtain an accuracy rate of 0.96."
