Detecting driver distraction

Abstract

The increasing use of in-vehicle information systems (IVISs), such as navigation devices and MP3 players, can jeopardize safety by introducing distraction into driving. One way to address this problem is to develop distraction mitigation systems, which adapt IVIS functions according to driver state. In such a system, correctly identifying driver distraction is critical, which is the focus of this dissertation. Visual and cognitive distractions are two major types of distraction that interfere with driving most compared with other types. Visual and cognitive distraction can occur individually or in combination. The research gaps in detecting driver distraction are that the interactions of visual and cognitive distractions have not been well studied and that no accurate algorithm/strategy has been developed to detect visual, cognitive, or combined distraction. To bridge these gaps, the dissertation fulfilled three specific aims. The first aim demonstrated the layered algorithm developed based on data mining methods could improve the detection of cognitive distraction from my previous studies. The second aim developed estimation algorithms for visual distraction and demonstrated a strong relationship of the estimated distraction with the increased risk of real crashes using the naturalistic data. The third objective examined the interaction of visual and cognitive distractions and developed an effective strategy to identify combined distraction. Together these aims suggest that driver distraction can be detected from performance indicators using appropriate quantitative methods. Data mining techniques represent a promising category of methods to construct such detection algorithms. When combined in a sequential way, visual distraction dominates the effects of distraction while cognitive distraction reduces the overall impairments of distraction on driver performance. Therefore, it is not necessary to detect cognitive distraction if visual distraction is present.