Drowsiness driving is one of the major reasons causing deadly traffic accidents in the United States of America. This paper intends to propose a system to detect different levels of drowsiness, which can help drivers to have enough time to handle sleepiness. Furthermore, we use distinct sound alarms to warn the user to prevent early accidents. The basis of the proposed approach is to consider symptoms of drowsiness, including the amount of eye closure, yawning, eye blinking, and head position to classify the level of drowsiness. We design a method to extract eye and mouth features from 68 key points of facial landmark. These features will help the system to detect the level of drowsiness in realtime video stream based on different symptoms. The experiential results show that the average accuracy of the system that has the capability to detect drowsiness intensity scale in different light conditions is approximately 96.6%.
Autonomous driving is an active area of research in the automotive market. The development of automated functions such as highway driving, autonomous parking etc. requires a robust platform for development and safety qualification of the system. In this context, virtual simulation platforms are key enablers for development of algorithms, software and hardware components. In this paper, we discuss multiple virtual simulation platforms such as open source car simulators, commercial automotive vendors and gaming platforms that are available in the market. We discuss the key factors that make the virtual platform suitable for automated driving function development. Based on the analysis of various simulation platforms, we end the paper with a proposal of two stage approach for the automated driving functionality development.