The on-road driving assessment is widely regarded as the criterion measure for driving performance despite a paucity of evidence concerning its psychometric properties. The purpose of this study was 2-fold. First, we examined the psychometric properties of an on-road driving assessment with 100 senior drivers between 60 and 86 years (80 healthy volunteers and 20 with specific vision deficits) using Rasch modeling. Second, we compared the outcome of the gestalt decision made by trained professionals with that based on weighted error scores from the standardized assessment. Rasch analysis provided good evidence for construct validity and inter-rater reliability of the on-road assessment and some evidence for internal reliability. Goodness of fit statistics for all items were within an acceptable range and the item hierarchy was logical. The test had a moderate reliability index (0.67). The best cut off score yielded sensitivity of 81% and specificity of 95% compared with the gestalt decision. Further research is required with less competent drivers to more fully examine reliability. Healthy senior drivers failed to check blind spots when changing lanes and made errors when asked to report road markings and traffic signs as they drove. In addition unsafe drivers had difficulty negotiating intersections and lane changes.
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http://dx.doi.org/10.1016/j.aap.2007.09.012 | DOI Listing |
PLoS One
December 2024
Tecnológico de Monterrey, Institute of Advanced Materials for Sustainable Manufacturing, Zapopan, Jalisco, México.
Advanced Driver Assistance Systems (ADAS) aim to automate transportation fully. A key part of this automation includes tasks such as traffic light detection and automatic braking. While indoor experiments are prevalent due to computational demands and safety concerns, there is a pressing need for research and development of new features to achieve complete automation, addressing real-world implementation challenges by testing them in outdoor environments.
View Article and Find Full Text PDFSensors (Basel)
November 2024
Yangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing 314019, China.
Intelligent transportation systems (ITSs) present new opportunities for enhanced traffic management by leveraging advanced driving behavior sensors and real-time information exchange via vehicle-based and cloud-vehicle communication technologies. Specifically, onboard sensors can effectively detect whether human-driven vehicles are adhering to traffic management directives. However, the formulation and validation of effective strategies for vehicle implementation rely on accurate driving behavior models and reliable model-based testing; in this paper, we focus on large roundabouts as the research scenario.
View Article and Find Full Text PDFTraffic Inj Prev
December 2024
Centre for Accident Research and Road Safety-Queensland (CARRS-Q), Queensland University of Technology (QUT), Brisbane, Queensland, Australia.
Objectives: In conditional automation for automated vehicles (AVs), drivers are tasked with remaining vigilant and ready to assume control should the system encounter a malfunction. However, little to no information is provided to the driver either about the AV's intended maneuvers or the AV's awareness of potential threats in the surrounding environment. To address this research gap, the present study proposes 2 human-machine interaction (HMI) concepts: Firstly, the shared intended pathway (SIP), which presents a forecast of the AV's intended maneuvers and, secondly, object recognition bounding boxes (ORBBs), which place transparent blue squares around other road users likely to contribute to a crash.
View Article and Find Full Text PDFPLoS One
December 2024
Institute of Advanced Technology Development, Hyundai Motor Company, Seongnam-si, Gyeonggi-do, South Korea.
Vehicle drivability, defined as the smooth operation and stability of a vehicle in response to driver inputs, significantly influences the performance of passenger cars. Among various driving conditions, tip-in acceleration is one of the most frequently encountered and crucial factors affecting drivability. This study investigates preferred longitudinal acceleration profiles for electric vehicles through subjective evaluations obtained from on-road tests.
View Article and Find Full Text PDFAccid Anal Prev
December 2024
Key Laboratory of Road and Traffic Engineering of the Ministry of Education, College of Transportation Engineering, Tongji University, 4800 Cao'an Highway, Shanghai, 201804, China. Electronic address:
Aesthetics has always been an advanced requirement in road environment design, because it can provide a pleasant driving experience and guide better driving behavior for human drivers. However, it remains unknown whether aesthetics-based road environment design also has an impact on autonomous vehicles (AVs), resulting in that current evaluation models on road readiness for AVs (RRAV) do not consider road environment aesthetics. Therefore, this study aims to explore the relationship between road environment aesthetics and risky driving behavior of AVs (RDBAV) and propose an RRAV evaluation model from the new perspective of road environment aesthetics.
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