Normally, autonomous vehicles (AVs) are limited to be widely used in market not only for technical factors, but also psychological reasons. Considering the psychological feelings of drivers during switching manned to unmanned operation modes, an algorithm for avoiding obstacles is designed for AVs by considering driver psychological feelings. A so-called confidence-limit-distance (denoted as CLD) for driver to avoid obstacle is experimentally obtained by a number of real track tests with 100 volunteer test drivers as required to approach the obstacle in a certain way. Based on Artificial Potential Field (APF) method, a road potential field is established accordingly to characterize the information on the real road. To express the different influences of obstacles on the driver’s psychological feeling in both longitudinal and lateral directions, a confidence potential field also is established based on a two-dimensional normal distribution combining von Mises distribution. Hence, the second-order Taylor expansions of the road potential field and the confidence potential field are firstly introduced into the cost functions for model predictive control (MPC). The corresponding MPC algorithm used here selects front-wheel steering angle as the control variable to be solved. The CLD and range of sensed vehicle motion state variables are taken as the constraints of the MPC. Co-simulations and Hardware-in-the-Loop (HIL) tests are carried out, showing the effectiveness of designed algorithm, which can be useful in the development and design for Advanced Driving Assistant System (ADAS) and AVs.
This study examines the determinants that drive the behavior of sharing health information within online health communities. Leveraging the Theory of Planned Behavior, the Technology Acceptance Model, and the “Knowledge-Attitude-Practice” theory, a comprehensive model elucidating the key elements that sway the health information-sharing behavior among users of online health communities is designed. This model is validated through Structural Equation Modeling (SEM) and Fuzzy Set Qualitative Comparative Analysis (fsQCA). Findings derived from the SEM suggest that perceived ease of use, perceived usefulness, perceived trust, and perceived behavioral control exert a significant positive impact on attitudes towards health information sharing, the intention to share health information, and the actual health information-sharing behavior. The fsQCA unfolds two unique configuration path models that lead to the emergence of health information-sharing behavior: one predicated on perceived trust and sharing intention, and the other on perceived usefulness, behavioral control, and sharing attitude. This research provides invaluable insights, fostering a deeper comprehension of the dynamics involved in health information sharing within online communities, thereby directing the design of more effective health platforms to augment user engagement and enable informed health decisions.
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