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Naturalistic driving recordings are important for understanding the driver behavior. Driver behavior events of interest in these recordings, such as driver confusion and stress, are important for studying driver behavior and develop the next generation advanced driver assistant systems (ADASs). Unfortunately, such events are rare cases in the naturalistic driving data. Manual annotation is usually...
This study explores the feasibility of detecting drivers' mirror-checking actions using noninvasive sensors. Checking the mirrors is an important primary driving action that allows drivers to maintain their situational awareness, particularly when they are planning to turn or change lanes. Recognizing when drivers are checking the mirrors can facilitate the detection of hazard scenarios by considering...
Drivers are exposed to a growing risk of being distracted with the recent development of in-vehicle systems for navigation, communication and infotainment. As a result, there is a need for tracking systems that can monitor the drivers' attention. This study investigates driver distractions using a multimodal corpus collected from real world driving scenarios. The paper focuses on facial cues automatically...
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