In the Internet-of-Things (IoT) era, billions of sensors and devices collect and process data
from the environment, transmit them to cloud centers, and receive feedback via the Internet …

A great lack of 5G design is the traditional bottom-up development of network evolution,
which has not effectively considered the requirements of applications and, particularly …

In recent years, various deep learning architectures have been proposed to solve complex
challenges (eg spatial dependency, temporal dependency) in traffic domain, which have …

The advent of in-vehicle networking systems as well as state-of-the-art sensors and
communication technologies have facilitated the collection of large volume and almost real …

Driver anomaly quantification is a fundamental capability to support human-centric driving
systems of intelligent vehicles. Existing studies usually treat it as a classification task and …

Driver attention estimation is one of the key technologies for intelligent vehicles. The existing
related methods only focus on the scene image or the driver's gaze or head pose. The …

Driver behavior is a critical factor in driving safety, making the development of sophisticated
distraction classification methods essential. Our study presents a Distracted Driving …

The paper presents an approach and case study of a distributed driver monitoring system.
The system utilizes smartphone sensors for detecting dangerous states for a driver in a …

In this paper, we propose simultaneous and sequential hybrid brain-computer interfaces
(hBCIs) that incorporate electroencephalography (EEG) and electromyography (EMG) …

Drunkenness or exhaustion is a leading cause of car accidents, with severe implications for
road safety. More fatal accidents could be avoided if fatigued drivers were warned ahead of …