Analyzing naturalistic driving data and self-reported crash data for car safety development

Real world data on driver behavior in normal driving and critical situations are essential for car safety development. Data collection and analysis methods that provide insight into the prevalence of crash contributing factors (e.g., drowsiness, distraction) and causation mechanisms are valuable when making priorities and selecting countermeasure principles. This thesis investigates different analysis methods applied to real world data from three sources: a crash mail survey, insurance claims, and naturalistic driving. Several analysis methods were investigated, including: adjusting for nonresponse in a crash mail survey, analyzing narratives provided by the involved road users in a crash, and investigating causation mechanisms based on video recordings of critical situations. Naturalistic driving data from whole trips were analyzed to investigate the influence of driving context (e.g., turning, other vehicles, speed) on drivers’ eye glance behavior and their exposure to visual-manual phone tasks. Insurance data proved useful for compensating for survey nonresponse bias related to crash types and driver demographics, while several crash contributing factors are likely to be underestimated in mail surveys due to issues regarding memory and social desirability. Narratives provided detailed additional information explaining why some of the crashes occurred. Video recordings of critical situations consistently revealed contributing factors related to drivers' visual behavior, the road environment, and the behavior of other road users, although drivers’ own thoughts and low vigilance were not identified. Naturalistic driving data collected continuously from whole trips were found to be an excellent source of information for studying normal driving behavior. Driving context influenced drivers’ eye glance behavior, task timing and overall propensity to engage in visual-manual phone tasks. In conclusion, no single source of real world data is sufficient on its own to prioritize crash types and contributing factors, and to select countermeasure principles. Future development should emphasize the analysis of large datasets from different sources, in order to provide insights into a wide range of crash contributing factors in different types of critical situations, including severe crashes