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This paper considers a comprehensive and collaborative project to collect large amounts of driving data on the road for use in a wide range of areas of vehicle-related research centered on driving behavior. Unlike previous data collection efforts, the corpora collected here contain both human and vehicle sensor data, together with rich and continuous(More)
Although, in recent years, significant developments have been made in road safety, traffic statistics indicate that we still need significant improvements in the field. Since traffic accidents usually reflect human factors, in this paper, we focus on clarifying the understanding of driver behaviors under hazardous scenarios. Brake pedal signals or driver(More)
This paper investigates a method for estimating a driver's spontaneous frustration in the real world. In line with a specific definition of emotion, the proposed method integrates information about the environment, the driver's emotional state, and the driver's responses in a single model. Driving data are recorded using an instrumented vehicle on which(More)
In this paper we present our latest achievements in the continuous estimation of a driver's spontaneous irritation. Experiments are conducted with data from 20 drivers, recorded under real driving conditions. While driving, participants also interact with a speech dialogue system to retrieve and play music. A fusion method is proposed to integrate(More)
In this paper we present our multimedia corpus of real-world driving data (NUDrive), built with the primary objective of firming foundations for applying digital signal processing technologies in the vehicular environment. NUDrive is a content rich corpus composed of driving, speech, video, and physiological signals. So far, we have collected data from 250(More)
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