Driver’s Distraction Detection via Hybrid CNN-LSTM

This study introduces a novel hybrid architecture consisting of a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to tackle the pressing problem of inattentive driving. Our approach leverages the temporal modeling capabilities of LSTMs and the spatial feature extraction capabilities of CNNs to perform real-time detection and analysis of various diversions, including but not limited to dining, phone calls, and messaging. Subsequent to undergoing training on an extensive dataset, the hybrid model attains an unprecedented level of accuracy, thereby promoting road safety for all. A convolutional neural network (CNN) processes visual input data while a long short-term memory (LSTM) component accumulates temporal correlations with the purpose of identifying and categorizing disruptive actions. Audio signals may assist motorists in refocusing their attention and mitigating potential dangers. This study not only signifies a substantial advancement in the domain of diversion detection but also holds promising implications for the progression of autonomous vehicles and advanced driver assistance systems (ADAS).