Abstract
With population growth, the demand for vehicles has increased tremendously, which has created an alarming situation in terms of traffic hazards and road accidents. The road accidents percentage is growing exponentially and so are the fatalities caused due to accidents. However, the primary cause of the increased rate of fatalities is due to the delay in emergency services. Many lives could be saved with efficient rescue services. The delay happens due to traffic congestion or unstable communication to the medical units. The implementation of automatic road accident detection systems to provide timely aid is crucial. Many solutions have been proposed in the literature for automatic accident detection. The techniques include crash prediction using smartphones, vehicular ad-hoc networks, GPS/GSM based systems, and various machine learning techniques. With such high rates of deaths associated with road accidents, road safety is the most critical sector that demands significant exploration. In this paper, we present a critical analysis of various existing methodologies used for predicting and preventing road accidents, highlighting their strengths, limitations, and challenges that need to be addressed to ensure road safety and save valuable lives.
Highlights
Due to rapid growth of world population, the demand for vehicles has increased tremendously, resultantly problems of traffic congestion and road accidents has increased
Machine learning techniques can be utilized to determine the severity of accident. Different algorithms such as k-means clustering, Support Vector Machine (SVM) under reinforcement learning by fetching real time data like velocity which is obtained by means of vibration sensors installed in vehicle and distance which is obtained by means of ultrasonic sensors [17]
This paper aims to fill this gap by critically reviewing the literature related to accident detection, prevention, and reporting systems, to provide a broader perspective of existing techniques so that effective systems can be developed that can utilize the strengths while addressing the challenges in the current systems
Summary
Due to rapid growth of world population, the demand for vehicles has increased tremendously, resultantly problems of traffic congestion and road accidents has increased. One technique focuses on using the accelerometer sensor, by monitoring the vehicle speed and report an accident as it reaches below the threshold point [13] Another approach aims on preventing drunken driving by installing alcohol sensors on the steering wheel and not allowing the driver to drive if he’s intoxicated by measuring the alcohol content in his oxygen [3], [14]. Machine learning techniques can be utilized to determine the severity of accident Different algorithms such as k-means clustering, SVM under reinforcement learning by fetching real time data like velocity which is obtained by means of vibration sensors installed in vehicle and distance which is obtained by means of ultrasonic sensors [17].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
