Driver distraction detection by in-vehicle signal processing

Driver distraction is one of the major causes of vehicle accidents. Many people have researched methods for reducing distraction of drivers and helping them to drive safely. Many studies have concerned products that monitor the state of drivers directly or indirectly and warn them of risk. In some previous studies, test subjects were forced to drive normally and inattentively to find the distinct feature patterns. However, the problem is that each driver can have different patterns in normal and abnormal driving. Moreover, in real driving conditions, they do not behave inattentively on purpose, and thus the patterns may not be replicated. In this paper, we present algorithms and experimental results that detect distraction by using in-vehicle signals without planned distraction. By using two kinds of machine learning schemes-unsupervised learning and supervised learning together-, normal and distracted driving features can be classified in real driving situation.