RESUMEN
The emergence of autonomous vehicles (AVs) marks a transformative leap in transportation technology. Central to the success of AVs is ensuring user safety, but this endeavor is accompanied by the challenge of establishing trust and acceptance of this novel technology. The traditional "one size fits all" approach to AVs may limit their broader societal, economic, and cultural impact. Here, we introduce the Persona-PhysioSync AV (PPS-AV). It adopts a comprehensive approach by combining personality traits with physiological and emotional indicators to personalize the AV experience to enhance trust and comfort. A significant aspect of the PPS-AV framework is its real-time monitoring of passenger engagement and comfort levels within AVs. It considers a passenger's personality traits and their interaction with physiological and emotional responses. The framework can alert passengers when their engagement drops to critical levels or when they exhibit low situational awareness, ensuring they regain attentiveness promptly, especially during Take-Over Request (TOR) events. This approach fosters a heightened sense of Human-Vehicle Interaction (HVI), thereby building trust in AV technology. While the PPS-AV framework currently provides a foundational level of state diagnosis, future developments are expected to include interaction protocols that utilize interfaces like haptic alerts, visual cues, and auditory signals. In summary, the PPS-AV framework is a pivotal tool for the future of autonomous transportation. By prioritizing safety, comfort, and trust, it aims to make AVs not just a mode of transport but a personalized and trusted experience for passengers, accelerating the adoption and societal integration of autonomous vehicles.
Asunto(s)
Conducción de Automóvil , Vehículos Autónomos , Humanos , Transportes , Tecnología , Personalidad , Emociones , Accidentes de TránsitoRESUMEN
In today's social network age, information flowing in networks does not derive solely from external sources; people in the network also independently generate signals. These self-generated signals may not be deliberate lies, but they may not bear any relationship with the truth, either. Following the philosopher Harry G. Frankfurt, we refer to such self-generated signals as bullshit. We present an information diffusion model that allows nodes which hold no value to spread information, capturing the diffusion of bullshit information. The presence of self-generated signals (i.e., bullshit) increases the amount of information available for transmission in the network. However, participants in the spread process respond to the existence of such self-generated information by receiving data from internal sources with caution. These two contradictory forces-the increase in information transmission on the one hand, and in suspicion on the other-result in a two-sided effect of bullshit on the total spread time. We first take a numerical approach, simulating our model on Watts-Strogatz networks and building a decision tree to characterize the effects of bullshit given different network structures. We find that increasing the rate of self-generated information may have either a monotonic or non-monotonic effect on the rumor spread time, depending on the network structure and rate of non-self-generated internal communications. Then, taking an analytical approach, we analyze the spread behavior for cliques, and identify the conditions for monotonic behavior in a 2-clique network.