The Multimodal Driver Monitoring Database: A Naturalistic Corpus to Study Driver Attention
This dataset addresses the critical need for in-domain data to train robust driver monitoring systems for in-vehicle safety, benefiting researchers and developers in autonomous driving and intelligent vehicle systems.
This paper introduces the Multimodal Driver Monitoring (MDM) dataset, a naturalistic corpus collected from 59 subjects, to study driver attention under challenging real-world conditions. The dataset includes continuous head movement tracking, predetermined gaze locations, common secondary activities, and recordings from RGB, depth cameras, and CAN-Bus data, aiming to provide a comprehensive resource for training driver monitoring algorithms.
A smart vehicle should be able to monitor the actions and behaviors of the human driver to provide critical warnings or intervene when necessary. Recent advancements in deep learning and computer vision have shown great promise in monitoring human behaviors and activities. While these algorithms work well in a controlled environment, naturalistic driving conditions add new challenges such as illumination variations, occlusions and extreme head poses. A vast amount of in-domain data is required to train models that provide high performance in predicting driving related tasks to effectively monitor driver actions and behaviors. Toward building the required infrastructure, this paper presents the multimodal driver monitoring (MDM) dataset, which was collected with 59 subjects that were recorded performing various tasks. We use the Fi- Cap device that continuously tracks the head movement of the driver using fiducial markers, providing frame-based annotations to train head pose algorithms in naturalistic driving conditions. We ask the driver to look at predetermined gaze locations to obtain accurate correlation between the driver's facial image and visual attention. We also collect data when the driver performs common secondary activities such as navigation using a smart phone and operating the in-car infotainment system. All of the driver's activities are recorded with high definition RGB cameras and time-of-flight depth camera. We also record the controller area network-bus (CAN-Bus), extracting important information. These high quality recordings serve as the ideal resource to train various efficient algorithms for monitoring the driver, providing further advancements in the field of in-vehicle safety systems.