This study aims to investigate the recent literature on auditory experiences within automotive environments and discusses potential future research directions in this area. Forty-six papers obtained through the PRISMA protocol were selected from literature published over the past 15 years. The collected literature was categorized based on engine type, and a comparative analysis of research trends in the automotive industry was conducted, explicitly focusing on internal combustion vehicles (ICVs) and electric vehicles (EVs). A network analysis was performed utilizing the keywords of the papers to identify the predominant research topics. The analysis revealed research topics actively studied in existing ICV research but not covered in EV and newly emerging research topics in the EV field. The study proposes future research topics related to auditory experience design. It aims to provide insight into the design of auditory experiences in automobiles, particularly as the automotive paradigm expands to include electric and autonomous vehicles.

This study aims to investigate the recent literature on auditory experiences within automotive environments and discusses potential future research directions in this area. Forty-six papers obtained through the PRISMA protocol were selected from literature published over the past 15 years. The collected literature was categorized based on engine type, and a comparative analysis of research trends in the automotive industry was conducted, explicitly focusing on internal combustion vehicles (ICVs) and electric vehicles (EVs). A network analysis was performed utilizing the keywords of the papers to identify the predominant research topics. The analysis revealed research topics actively studied in existing ICV research but not covered in EV and newly emerging research topics in the EV field. The study proposes future research topics related to auditory experience design. It aims to provide insight into the design of auditory experiences in automobiles, particularly as the automotive paradigm expands to include electric and autonomous vehicles.

The increasing integration of Artificial Intelligence (AI) into team-based decision environments necessitates an examination of trust formation that extends beyond the individual user. This context introduces complexities stemming from interpersonal dynamics, heterogeneous individual trust levels, and collective decision processes. This study investigates how group decision-making processes change AI trust dynamics compared to individual settings. We aimed to develop a theoretical framework capturing these multilayered trust dynamics using structural equation modeling. We employed Körber’s Trust in Automation (TiA) scale pre- and post-task. A total of 51 participants, organized into 16 teams, performed a collaborative decision-making exercise (NASA moon survival) using ChatGPT. Structural equation modeling (SEM) was used for analysis. Group trust formation patterns significantly diverged from individual contexts. Attitudinal trust, strongly influenced by collective pe rceptions of AI performance and reliability, was the primary predictor of overall group trust, outweighing behavioral trust (actual usage). Factors like understanding/predictability showed no significant influence in group settings. Group-level dynamics fundamentally alter AI trust formation, challenging individual-centric views. Practical implications include the need for trust-building strategies focused on collective perceptions and experiences. The findings underscore the need for new theoretical models and group-specific trust measurement tools.

This study investigates the impact of three Auditory User Interfaces (AUIs) on takeover performance and mental workload in autonomous vehicles (AVs). As AVs reduce the need for driver engagement, users can focus on non-driving-related activities (NDRAs), which often require visual attention. When a takeover is needed, auditory communication plays a crucial role in ensuring effective information delivery. This research examines how different levels of auditory explanations for takeover requests influence driver response, considering initial trust in automation (TiA). Twenty participants (8 females, 12 males) were categorized into high- and low-TiA groups. Each experienced different AUIs across eight driving trials while performing a typing task as an NDRA in a simulator. Takeover reaction time, glance frequency, and perceived mental workload were measured to evaluate takeover performance and cognitive demand. Results revealed performance differences between user groups, offering insights into trust-aware auditory interaction design for AVs in NDRA scenarios.

The influence of auditory environments on task performance has been widely studied through cognitive models that predict interference effects. This study examines whether lyrics in preferred background music affect workplace productivity, particularly typing accuracy and speed. Twenty-two participants completed a typing task under silence and self-selected music. They were grouped by auditory work preferences - favoring lyrics or instrumental audio. While typing speed remained stable, accuracy results contradicted established models. Baddeley’s Working Memory Model predicts verbal interference, Cognitive Load Theory suggests added cognitive burden and Wickens’ Multiple Resource Theory posits competition for verbal processing resources. However, participants preferring lyrical music showed improved accuracy. These findings suggest familiarity and personal preference mitigate expected interference, challenging conventional cognitive models. This study highlights the need to reconsider cognitive frameworks in workplace ergonomics and emphasizes the role of personalized auditory environments in optimizing task efficiency.

In conditionally automated vehicles (CAVs), takeover requests (ToRs) must re-engage drivers quickly and effectively. This study examines how the type of ToR explanation (action-only, precursor-only, hazard-only, precursor+hazard) and hazard type (behavioral vs. environmental) jointly affect driver’s situation awareness, situational trust, and cognitive workload. In a driving simulator experiment using a 4×2 within-subjects design, 12 participants experienced all conditions while engaged in non-driving tasks. The findings suggest that while more informative explanations significantly improved situational awareness, trust, and satisfaction, they also imposed a greater cognitive workload. Behavioral hazards consistently led to better outcomes, whereas environmental hazards were more sensitive to explanation quality. Significant interaction effects emerged across most measures. This study underscores the importance of adaptive, context-aware ToR interfaces that adjust information to both the driving environment and cognitive demands. Balancing informativeness with mental workload is key to enhancing safety and trust in automated driving systems.