Real-Time Distracted Drivers Detection Using Deep Learning

Safety implications of providing real-time feedback to distracted drivers.

A driving simulator study was conducted to assess whether real-time feedback on a driver's state can influence the driver's interaction with in-vehicle information systems (IVIS). Previous studies have shown that IVIS tasks can undermine driver safety by increasing driver distraction. Thus, mitigating driver distraction using a feedback mechanism appears promising. This study was designed to te...

Concept drift detection in business process logs using deep learning

Process mining provides a bridge between process modeling and analysis on the one hand and data mining on the other hand. Process mining aims at discovering, monitoring, and improving real processes by extracting knowledge from event logs. However, as most business processes change over time (e.g. the effects of new legislation, seasonal effects and etc.), traditional process mining techniques ...

Doubletalk Detection Using Real Time Recurrent Learning

In this paper we present a new system for doubletalk detection that uses multiple signal detectors/discriminators based on recurrent networks. The goal is to build a simple system that learns to combine information from different signal sources to make robust decisions even under changing noise conditions. In this paper we use three detectors two of these are frequency domain signal detectors, ...

Distracted Drivers and Unattended Experience

Consider the much-discussed case of the distracted driver, who is alleged to successfully navigate his car for miles despite being completely oblivious to his visual states. Perhaps he is deeply engrossed in the music playing over the radio or in philosophical reflection, and as a result he goes about unaware of the scene unfolding before him on the road. That the distracted driver has visual e...

Detection of Congenital Mullerian Anomalies Using Real-Time 3D Sonography