Review and perspectives on driver digital twin and its enabling technologies for intelligent vehicles
Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving
and transportation systems to digitize and synergize connected automated vehicles …
and transportation systems to digitize and synergize connected automated vehicles …
Detecting and recognizing driver distraction through various data modality using machine learning: A review, recent advances, simplified framework and open …
Driver distraction is one of the main causes of fatal traffic accidents. Therefore, the ability to
detect driver inattention is essential in building a safe yet intelligent transportation system …
detect driver inattention is essential in building a safe yet intelligent transportation system …
A survey on multimodal large language models for autonomous driving
With the emergence of Large Language Models (LLMs) and Vision Foundation Models
(VFMs), multimodal AI systems benefiting from large models have the potential to equally …
(VFMs), multimodal AI systems benefiting from large models have the potential to equally …
A multimodal approach to estimating vigilance using EEG and forehead EOG
Objective. Covert aspects of ongoing user mental states provide key context information for
user-aware human computer interactions. In this paper, we focus on the problem of …
user-aware human computer interactions. In this paper, we focus on the problem of …
A temporal–spatial deep learning approach for driver distraction detection based on EEG signals
Distracted driving has been recognized as a major challenge to traffic safety improvement.
This article presents a novel driving distraction detection method that is based on a new …
This article presents a novel driving distraction detection method that is based on a new …
Classification of driver cognitive load: Exploring the benefits of fusing eye-tracking and physiological measures
In-vehicle infotainment systems can increase cognitive load and impair driving performance.
These effects can be alleviated through interfaces that can assess cognitive load and adapt …
These effects can be alleviated through interfaces that can assess cognitive load and adapt …
LGGNet: Learning from local-global-graph representations for brain–computer interface
Neuropsychological studies suggest that co-operative activities among different brain
functional areas drive high-level cognitive processes. To learn the brain activities within and …
functional areas drive high-level cognitive processes. To learn the brain activities within and …
Quantitative evaluation of attraction intensity of highway landscape visual elements based on dynamic perception
X Qin, M Fang, D Yang, VW Wangari - Environmental Impact Assessment …, 2023 - Elsevier
In order to quantify the aesthetic attraction of visual elements of highway landscape space,
the concept of visual attraction intensity of highway landscape space is proposed, and the …
the concept of visual attraction intensity of highway landscape space is proposed, and the …
Distinguishing mental attention states of humans via an EEG-based passive BCI using machine learning methods
Recent advances in technology bring about novel operating environments where the role of
human participants is reduced to passive observation. While opening new frontiers in …
human participants is reduced to passive observation. While opening new frontiers in …
Motor-imagery-based brain–computer interface using signal derivation and aggregation functions
Brain–computer interface (BCI) technologies are popular methods of communication
between the human brain and external devices. One of the most popular approaches to BCI …
between the human brain and external devices. One of the most popular approaches to BCI …