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As the proportion of road accidents increases each year, driver distractioncontinues to be an important risk component in road traffic injuries anddeaths. The distractions caused by the increasing use of mobile phones andother wireless devices pose a potential risk to road safety. Our current studyaims to aid the already existing techniques in driver posture recognition byimproving the performance in the driver distraction classification problem. Wepresent an approach using a genetic algorithm-based ensemble of six independentdeep neural architectures, namely, AlexNet, VGG-16, EfficientNet B0, VanillaCNN, Modified DenseNet, and InceptionV3 + BiLSTM. We test it on twocomprehensive datasets, the AUC Distracted Driver Dataset, on which ourtechnique achieves an accuracy of 96.37%, surpassing the previously obtained95.98%, and on the State Farm Driver Distraction Dataset, on which we attain anaccuracy of 99.75%. The 6-Model Ensemble gave an inference time of 0.024seconds as measured on our machine with Ubuntu 20.04(64-bit) and GPU as GeForceGTX 1080.

A Computer Vision-Based Approach for Driver Distraction Recognition using Deep Learning and Genetic Algorithm Based Ensemble