A detection model of aggressive driving behavior based on hybrid deep learning - Khalid, Noor Walid; Abdullah, Wisam Dawood (Institute of Advanced Engineering and Science, 2024)
Modern transportation faces a crucial challenge in ensuring road safety by addressing driving behavior concerns. This paper introduces an innovative deep learning model derived from a cellphone-collected Driving Behavior dataset, focusing on detecting and classifying aggressive driving. Using a cohort-based dataset, a hyper-deep learning model categorizes drivers into normal, slow, and aggressive groups. The system employs pre-processing methods and two methodologies, directly inputting data and incorporating feature selection. The hyper-CNN-Dense model, used for training, shows promising results. Feature selection techniques like SVD6 and MI6 achieve optimal outcomes, with a 100% accuracy rate in detecting aggressive driving. Notably, SVD6 boasts a short processing time of just 43 seconds. This research successfully identifies aggressive driving behavior with impeccable accuracy and in a remarkably short timeframe.