This study examined whether speech-based interfaces for different in-vehicle-information-systems (IVIS) reduce the distraction caused by these systems. For three frequently used systems (audio, telephone with name selection, navigation system with address entry and point of interest selection) speech, manual control and driving without IVIS (baseline) were compared. The Lane Change Task was used to assess driving performance. Additionally, gaze behavior and a subjective measure of distraction were analyzed. Speech interfaces improved driving performance, gaze behavior and subjective distraction for all systems with the exception of point-of-interest entry. However, these improvements were overall not strong enough to reach the baseline performance level. Only in easy segments of the driving task the performance level was comparable to baseline. Thus, speech-based IVIS have to be further developed to keep the cognitive complexity at an adequate level which does not disturb driving. However, looking at the benefits, speech control is a must for the car of the future.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.aap.2009.05.007 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Tissue-Specific DNA Methylation Changes in CD8 T Cells During Chronic Simian Immunodeficiency Virus Infection of Infant Rhesus Macaques.
Viruses
November 2024
Department of Microbiology and Immunology, University of North Carolina, Chapel Hill, NC 27599, USA.
Robust CD8 T cell responses are critical for the control of HIV infection in both adults and children. Our understanding of the mechanisms driving these responses is based largely on studies of cells circulating in peripheral blood in adults, but the regulation of CD8 T cell responses in tissue sites is poorly understood, particularly in pediatric infections. DNA methylation is an epigenetic modification that regulates gene transcription.
View Article and Find Full Text PDFDisturbance Robust Attitude Stabilization of Multirotors with Control Moment Gyros.
Sensors (Basel)
December 2024
Department of Aerospace Engineering, Chosun University, Gwangju 61452, Republic of Korea.
This paper presents a novel control framework for enhancing the attitude stabilization of multirotor UAVs using Control Moment Gyros (CMGs) and a Disturbance Robust Drive Law (DRDL). Due to their lightweight and compact structure, multirotor UAVs are highly susceptible to disturbances such as wind, making it challenging to achieve stable attitude control using rotor thrust alone. To address this issue, we employ CMGs to provide robust attitude control and apply Fast Terminal Sliding Mode Control (FTSMC) to ensure fast and accurate convergence within a finite time.
View Article and Find Full Text PDFAttention-Based PSO-LSTM for Emotion Estimation Using EEG.
Sensors (Basel)
December 2024
Department of Information and Electronic Engineering, International Hellenic University, 57001 Thessaloniki, Greece.
Recent advances in emotion recognition through Artificial Intelligence (AI) have demonstrated potential applications in various fields (e.g., healthcare, advertising, and driving technology), with electroencephalogram (EEG)-based approaches demonstrating superior accuracy compared to facial or vocal methods due to their resistance to intentional manipulation.
View Article and Find Full Text PDFLane Detection Based on ECBAM_ASPP Model.
Sensors (Basel)
December 2024
School of Artificial Intelligence and Computer Science, Nantong University, Nantong 226019, China.
With the growing prominence of autonomous driving, the demand for accurate and efficient lane detection has increased significantly. Beyond ensuring accuracy, achieving high detection speed is crucial to maintaining real-time performance, stability, and safety. To address this challenge, this study proposes the ECBAM_ASPP model, which integrates the Efficient Convolutional Block Attention Module (ECBAM) with the Atrous Spatial Pyramid Pooling (ASPP) module.
View Article and Find Full Text PDFAdaptive Memory-Augmented Unfolding Network for Compressed Sensing.
Sensors (Basel)
December 2024
School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.
Deep unfolding networks (DUNs) have attracted growing attention in compressed sensing (CS) due to their good interpretability and high performance. However, many DUNs often improve the reconstruction effect at the price of a large number of parameters and have the problem of feature information loss during iteration. This paper proposes a novel adaptive memory-augmented unfolding network for compressed sensing (AMAUN-CS).
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!