Graphical User Interface for intelligent automotive with vehicle to vehicle communication and adaptive light controls using image processing and machine learning

Abstract

Navigation systems are a vital part of the current traffic system. Advancements in driving technology have brought about radical changes in driving behaviour and reduced routing time. However, there are also risks to users in terms of distraction or inattention. Driving in night possess more risk than in day time driving due to abuse of high beams of the head lamb. Similar to the night driving, travelling in foggy day is also difficult for all road user. As far as the disruptive effects of navigation systems are concerned, the empirical conclusions are heterogeneous. The project is aimed to develop a low and effective Advance driver assistance system which includes vehicle to vehicle communication and intelligent headlight control. The project also aims to study and analyse different multi-disciplinary techniques which include supervised machine learning techniques o effectively classify road surface conditions using data collected from smartphones to ensure a safe and comfortable driving. A Graphical User Interface was developed which increases the usability of the system. In particular, visual distraction caused by navigation systems in relation to map navigation was reviewed. The project aims to analyse the data in such a way as to improve road safety when using a navigation system in unfamiliar areas. The results show that less glances of more than 2 seconds were found on the navigation system while map navigation leads to higher off-road times.