Impaired driving detection based on deep convolutional neural network using multimodal sensor data
Intelligent vehicle systems, such as advanced driving assistance systems, are relatively popular nowadays, which facilitate the timely prevention of driving-related accidents and human injuries caused by impaired driving. An often ignored problem in existing systems is the heterogeneousness across multimodal sensor data, which is becoming more crucial due to the widespread use of more different sensors. This paper proposes a novel feature fusion based detection approach using deep convolutional neural network to profile driver-related, vehicle-related, and road-related features, and extract collaborative information from them. Experimental results demonstrate that the proposed approach is capable of providing more accurate detection of impaired driving, compared with that achieved by other state-of-the-art classifier.