  As the proportion of road accidents increases each year, driver distraction
continues to be an important risk component in road traffic injuries and
deaths. The distractions caused by the increasing use of mobile phones and
other wireless devices pose a potential risk to road safety. Our current study
aims to aid the already existing techniques in driver posture recognition by
improving the performance in the driver distraction classification problem. We
present an approach using a genetic algorithm-based ensemble of six independent
deep neural architectures, namely, AlexNet, VGG-16, EfficientNet B0, Vanilla
CNN, Modified DenseNet, and InceptionV3 + BiLSTM. We test it on two
comprehensive datasets, the AUC Distracted Driver Dataset, on which our
technique achieves an accuracy of 96.37%, surpassing the previously obtained
95.98%, and on the State Farm Driver Distraction Dataset, on which we attain an
accuracy of 99.75%. The 6-Model Ensemble gave an inference time of 0.024
seconds as measured on our machine with Ubuntu 20.04(64-bit) and GPU as GeForce
GTX 1080.
