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Naturalistic driving studies (NDSs) capture large volumes of drive data from multiple sensor modalities, which are analyzed for critical information about driver behavior and driving characteristics that lead to crashes and near crashes. One of the key steps in such studies is data reduction, which is defined as a process by which "trained employees" review segments of driving video and record a taxonomy of variables that provides information regarding the sequence of events leading to crashes. Given the volume of sensor data in NDSs, such manual analysis of the drive data can be time-consuming. In this paper, we introduce "drive analysis" as one of the first steps toward automating the process of extracting midlevel semantic information from raw sensor data. Techniques are proposed to analyze the sensor data from multiple modalities and to extract a set of 23 semantics about lane positions, vehicle localization within lanes, vehicle speed, traffic density, and road curvature. The proposed techniques are demonstrated using real-world test drives comprising over 150 000 frames of visual data, which are also accompanied by vehicle dynamics that are captured from an in-vehicle controller-area-network bus, an inertial motion unit, and a Global Positioning System. Index Terms-Automatic drive analysis, lane-change detection, lane characteristics, naturalistic driving studies (NDSs), speed violation detection, traffic scenario detection.1524-9050

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