VEHICLE NAVIGATION USING ADVANCED OPEN SOURCE COMPUTER VISION

Abstract

The current operational transport and vehicle systems consist of vehicles running on fossil fuels or battery powered systems. The navigation requires control by a human driver who is responsible for a safe and comfortable journey from one place to another. However, with human intervention there are several drawbacks that may lead to a poor performance by the system. Negligence in driving leading to fatal accidents, environmental damage, infrastructure damage and destruction, health problems due to constrained sitting postures, long duration of operation and several others, have motivated researchers to look for solutions that will automate the driving process. Considering all these shortcomings of current systems, the new research consists of the use of self driving cars for transport and navigation. The complexity of this problem was seen when the initial systems were built using machine learning techniques that tried to understand and model the dynamic nature of the environment. As the research progressed, we realized that the system must be trained to respond to a number of unpredictable situations such as rain, snow, lightning, oil spills, potholes, passerby pedestrians and animals, approaching vehicles and many more. We need to consider all these aspects before a fully functional real-time system can be used. We consider the problem of autonomous vehicle by focusing on three major aspects of any self driving car which form the foundation of the entire system. Firstly, we need to be able to detect the lane lines so that our vehicle can orient itself correctly and continue to follow a safe path while being aware of the dynamic environment. Further, it needs to know its departure from the center of the lane in the scenario that it needs to move in order to avoid potholes or other road obstacles.