Comparative analysis of machine learning techniques for enhanced vehicle tracking and analysis

Abstract

The past few years have seen a marvellous growth in technology and science. This rapid improvement has proven to be a blessing, making human life easier. Technological developments such as autonomous driving systems and electric cars have made it easier to travel in a dependable and economical manner, satisfying the increasing need for convenient and environmentally friendly travel. However, the increase in traffic has led to a surge in accidents and road casualties. Despite efforts to enhance automobile design and traffic control, there remains a significant need for implementing a system for vehicle tracking, accident detection, and notification. Delays in information and unfulfilled medical needs often result in the loss of lives following accidents. This study reviews and compares different automatic accident detection and notification systems that use accelerometers, vibration detectors, and GPS technology to notify registered contacts of an accident's location via SMS or email. The analysis that follows will specifically look at the benefits, drawbacks, and future uses of various technologies that are used in these systems. In this study, different machine learning-based methods for improving the accuracy of car tracking and cutting down on reaction times in accident situations will be looked at and compared. For testing their usefulness, we used deep learning models like CNN, SVM, and YOLOv3 on a number of different datasets. According to our data, these methods greatly enhance the accuracy of spotting, with YOLOv3 showing the best level of accuracy. Furthermore, the study talks about the pros, cons, and possible future uses of these technologies. It stresses the need for more research into improving model performance in different situations.