Using transfer learning and model fusion method to detect distracted drivers

The World Health Organization (WHO), reports that well over 135 million people die worldwide in road traffic accidents each year and that the main cause of most accidents is driver distraction. Distractions such as using mobile phones and other gadgets such as navigation and sound systems, talking to passengers, eating and drinking, all constitute risky behavior while driving. A system that can automatically detect such distracted driving and alert a driver to their risky behavior could reduce the risk of …

accidents automotive distractions driver fusion gadgets health mobile mobile phones navigation people phones reports sound systems traffic transfer transfer learning world world health organization