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Older adults' road safety is a concern given the ageing population and increasing numbers of licensed older drivers. Advanced Driver-Assistance Systems (ADAS) are designed to improve safety, however little is known about the relationship between ADAS use and its trust and acceptance in older adults. The purpose of this study was to assesses an instrument of older driver acceptance of and trust in ADAS. A survey distributed to 1008 older Australian drivers (M = 72.1, SD = 6.94) found there was an overwhelmingly positive attitude towards ADAS, however trust in the systems were low and drivers had concerns with privacy, safety and failure of the systems. The Partial Automation Acceptance Scale was validated, producing a four-factor model measuring attitudes towards ADAS, attitudes towards technology, trust and perceptions on risk. Multiple regression showed three of the four factors predict use of ADAS, providing preliminary evidence of the validity and reliability of the scale.
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http://dx.doi.org/10.1016/j.apergo.2025.104474 | DOI Listing |
PLoS One
September 2025
Graduate School of Information, Yonsei University, Seoul, South Korea.
With the introduction of Advanced Driver Assistance Systems (ADAS), modern vehicles are equipped with numerous sensors, significantly increasing data communication within the in-vehicle network. However, the limited bandwidth of the Controller Area Network (CAN) poses challenges for high-speed sensor data transmission. To address this, automotive ethernet is emerging as a replacement for CAN, enabling the efficient transmission of large volumes of data, such as from cameras and LiDAR.
View Article and Find Full Text PDFSensors (Basel)
August 2025
Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Tokyo 113-8656, Japan.
Crossing collisions between cyclists and automobiles around nonsignalized intersections on community roads, where visibility around the intersection is poor due to occlusions caused by house walls, is a social issue related to traffic safety in Japan. Because available observation information for collision prevention is limited on community roads, utilizing the accumulated data is useful to compensate for the lack of observation information. Given these motivations, we propose a movement estimation method of cyclists by combining information from roadside sensors with location-dependent statistical information.
View Article and Find Full Text PDFAtten Percept Psychophys
August 2025
School of Mechanical Engineering, Tianjin University, No.135 Yaguan Road, Jinnan District, Tianjin, 300350, China.
The visual perception and steering behavior of drivers are known to be influenced by environmental lighting, but the underlying perception mechanisms, particularly the role of optical flow under low-luminance conditions, remain insufficiently understood. In a simulated driving experiment, 32 participants were exposed to five controlled luminance levels while their eye movements and driving performance were recorded. The results indicate that lower environmental luminance leads to prolonged gaze duration, a wider distribution of gaze points, and an increase in lateral steering errors.
View Article and Find Full Text PDFAm J Occup Ther
September 2025
Abbie Hutton, PhD, is Research Assistant, Envision Research Institute, Wichita, KS. At the time of this research, Hutton was PhD Student, Department of Psychology, Wichita State University, Wichita, KS.
Importance: Older drivers with impaired vision may face challenges using in-vehicle technologies, affecting user experience.
Objective: To explore use challenges with in-vehicle technologies and training preferences of older drivers with and without central vision loss (CVL).
Design: A telephone questionnaire on perceptions of driver-vehicle interface use difficulties, challenges with existing advanced driver assistance systems (ADAS), and preferences for ADAS acquisition and training.
Sci Rep
August 2025
School of Fine Arts, Nanjing Normal University, Nanjing, 210023, China.
This study is based on the extended Technology Acceptance Model, integrating Habit Theory and Regret Theory to construct a model of users' continuous usage intentions. It conducts a comparative analysis of users' continuous usage intentions for both assisted driving systems and driverless systems. Data was collected through an online questionnaire survey and analyzed using Structural Equation Modeling.
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