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Mrs. B. Ramyasree is an Assistant Professor in the Department of Electronics and Communication Engineering at Gurunanak Institute of Technology, Hyderabad, Telangana, India. She is actively involved in teaching and research in communication engineering, Artificial Intelligence, IoT, and intelligent transportation technologies. Her research focuses on AI-enabled smart systems and emerging engineering applications.
Road traffic collisions rank among the foremost contributors to deaths, injuries, and financial detriment globally. Accelerated urban development, rising automobile ownership, driver conduct, unfavourable weather conditions, inadequate road infrastructure, and traffic congestion have markedly heightened the intricacy of road safety administration. Traditional accident prevention methods predominantly depend on past accident documentation and human traffic surveillance, constraining their capacity to proactively recognise high-risk scenarios. The amalgamation of Deep Learning (DL), Artificial Intelligence (AI), Internet of Things (IoT), Computer Vision, Edge Computing, Cloud Computing, and Intelligent Transportation Systems (ITS) has revolutionised the forecasting and prevention of road accidents through real-time surveillance, astute risk evaluation, traffic behaviour analysis, and automated decision-making support. Deep learning frameworks like Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Vision Transformers (ViT), Graph Neural Networks (GNN), and Hybrid Deep Learning models markedly enhance the precision of accident forecasting by scrutinising traffic imagery, video feeds, sensor inputs, meteorological data, and vehicle telemetry. This chapter elucidates the principles, framework, deep learning methodologies, applications, obstacles, and prospective research avenues of deep learning for road accident prediction and prevention.