Enhancing Road Safety Using the Internet of Vehicles: A Machine Learning-Based Collision Detection Approach

Traffic accidents have been one of the most common causes of fatalities. With the advance of technologies and Machine Learning (ML), the Internet of Vehicles (IoV) has recently thrived tremendously. In this paper, we examine the use of ML techniques in detecting traffic collisions according to several factors using IoV technology. We have used a public dataset to build and evaluate several ML models including ANN and Deep Learning (DL). The accuracy of the best-achieved results on the testing dataset is 94.4%. Additionally, we provide a study of the importance of each parameter on the collision. The obtained results are promising in predicting forward collisions and could contribute to minimizing traffic accidents when using ML and IoV.